Deep learning classifiers for hyperspectral imaging: A review
暂无分享,去创建一个
Mercedes Eugenia Paoletti | A. Plaza | J. M. Haut | M. E. Paoletti | J. Plaza | A. Plaza | J. Plaza | J. Haut
[1] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[2] John R. Jensen,et al. Opening the black box of neural networks for remote sensing image classification , 2004 .
[3] Arto Kaarna,et al. Unsupervised Multi-manifold Classification of Hyperspectral Remote Sensing Images with Contractive Autoencoder , 2017, SCIA.
[4] Qian Du,et al. Unsupervised Spatial–Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[5] Chao Li,et al. Active Transfer Learning Network: A Unified Deep Joint Spectral–Spatial Feature Learning Model for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[6] Gregory R. Koch,et al. Siamese Neural Networks for One-Shot Image Recognition , 2015 .
[7] P. Switzer,et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .
[8] M. Bauer,et al. Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: the Mississippi River and its tributaries in Minnesota. , 2013 .
[9] Ilya Sutskever,et al. Training Deep and Recurrent Networks with Hessian-Free Optimization , 2012, Neural Networks: Tricks of the Trade.
[10] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[11] Naftali Tishby,et al. Opening the Black Box of Deep Neural Networks via Information , 2017, ArXiv.
[12] Peng Liu,et al. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1 Active Deep Learning for Classification of Hyperspectral Images , 2022 .
[13] Xiuwen Liu,et al. Land Cover Classification from Multi-temporal, Multi-spectral Remotely Sensed Imagery using Patch-Based Recurrent Neural Networks , 2017, Neural Networks.
[14] Baocai Yin,et al. Hyperspectral Image Classification Based on Deep Deconvolution Network With Skip Architecture , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[15] Xiao Xiang Zhu,et al. Deep Recurrent Neural Networks for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[16] H. Buhl,et al. A Short Survey , 1986 .
[17] Dexiang Zhang,et al. Hyperspectral Image Classification Using Spatial and Edge Features Based on Deep Learning , 2019, Int. J. Pattern Recognit. Artif. Intell..
[18] Yi Lu,et al. Auto-Encoder Based for High Spectral Dimensional Data Classification and Visualization , 2017, 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC).
[19] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .
[20] Ali Farhadi,et al. Unsupervised Deep Embedding for Clustering Analysis , 2015, ICML.
[21] Jiang Li,et al. Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction , 2002, IEEE Trans. Geosci. Remote. Sens..
[22] P. K Varshney,et al. Advanced image processing techniques for remotely sensed hyperspectral data : with 128 figures and 30 tables , 2004 .
[23] J. Roger,et al. Analysis of the uncertainties affecting predictions of clay contents from VNIR/SWIR hyperspectral data , 2015 .
[24] Liang Xiao,et al. Supervised Spectral–Spatial Hyperspectral Image Classification With Weighted Markov Random Fields , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[25] Daniel Mozos,et al. FPGA implementation of the principal component analysis algorithm for dimensionality reduction of hyperspectral images , 2019, Journal of Real-Time Image Processing.
[26] N. Mazhari,et al. Detecting and mapping different types of iron mineralization in Sangan mining region, NE Iran, using satellite image and airborne geophysical data , 2017, Geosciences Journal.
[27] Qingshan Liu,et al. Hyperspectral Image Classification Using Spectral-Spatial LSTMs , 2017, CCCV.
[28] Ronen Basri,et al. SpectralNet: Spectral Clustering using Deep Neural Networks , 2018, ICLR.
[29] Shaoguang Zhou,et al. Semisupervised Stacked Autoencoder With Cotraining for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[30] Suhad Lateef Al-khafaji,et al. Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images , 2018, IEEE Transactions on Image Processing.
[31] Kandarpa Kumar Sarma,et al. Hyperspectral Remote Sensing Classifications: A Perspective Survey , 2016, Trans. GIS.
[32] K. Strimmer,et al. Optimal Whitening and Decorrelation , 2015, 1512.00809.
[33] Linmi Tao,et al. Efficient Deep Auto-Encoder Learning for the Classification of Hyperspectral Images , 2016, 2016 International Conference on Virtual Reality and Visualization (ICVRV).
[34] Lei Huang,et al. Decorrelated Batch Normalization , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[35] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[36] Jon Atli Benediktsson,et al. Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis , 2011, IEEE Geoscience and Remote Sensing Letters.
[37] A F Goetz,et al. Imaging Spectrometry for Earth Remote Sensing , 1985, Science.
[38] Susan L. Ustin,et al. Evaluation of the potential of Hyperion data to estimate wildfire hazard in the Santa Ynez front range, Santa Barbara, California , 2002, IEEE International Geoscience and Remote Sensing Symposium.
[39] Edward R. Dougherty,et al. The peaking phenomenon in the presence of feature-selection , 2008, Pattern Recognit. Lett..
[40] Antonio J. Plaza,et al. Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[41] Xiuping Jia,et al. Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[42] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[43] Sen Wang,et al. Alternately Updated Spectral-Spatial Convolution Network for the Classification of Hyperspectral Images , 2019, Remote. Sens..
[44] Xi-Lin Li,et al. Preconditioned Stochastic Gradient Descent , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[45] Joost van de Weijer,et al. Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[46] Ignacio Requena,et al. Are artificial neural networks black boxes? , 1997, IEEE Trans. Neural Networks.
[47] Fan Zhang,et al. Deep Convolutional Neural Networks for Hyperspectral Image Classification , 2015, J. Sensors.
[48] P. Fisher. The pixel: A snare and a delusion , 1997 .
[49] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[50] Sotiris B. Kotsiantis,et al. Machine learning: a review of classification and combining techniques , 2006, Artificial Intelligence Review.
[51] Bruno A. Olshausen,et al. Learning Sparse Codes for Hyperspectral Imagery , 2011, IEEE Journal of Selected Topics in Signal Processing.
[52] Jon Atli Benediktsson,et al. Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data , 1993 .
[53] Qi Li,et al. Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features , 2016, Remote. Sens..
[54] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[55] Onisimo Mutanga,et al. Random Forests Unsupervised Classification: The Detection and Mapping of Solanum mauritianum Infestations in Plantation Forestry Using Hyperspectral Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[56] Tao Li,et al. Cube-CNN-SVM: A Novel Hyperspectral Image Classification Method , 2016, 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI).
[57] Stein M. Nornes,et al. First hyperspectral imaging survey of the deep seafloor: High-resolution mapping of manganese nodules , 2018 .
[58] Patrick Hostert,et al. Environmental Mapping and Analysis Program (EnMAP) - Recent Advances and Status , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.
[59] Matthias Bethge,et al. Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet , 2019, ICLR.
[60] Xian-zhong Shi,et al. Using hyperspectral data and PLSR modelling to assess acid sulphate soil in subsurface , 2014, Journal of Soils and Sediments.
[61] David J. C. MacKay,et al. Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.
[62] Shuyuan Yang,et al. Data-Driven Compressive Sampling and Learning Sparse Coding for Hyperspectral Image Classification , 2014, IEEE Geoscience and Remote Sensing Letters.
[63] Deyu Meng,et al. Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network , 2017, IEEE Transactions on Image Processing.
[64] Jie Geng,et al. Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[65] Jessica A. Faust,et al. Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .
[66] Jocelyn Chanussot,et al. Noise Reduction in Hyperspectral Imagery: Overview and Application , 2018, Remote. Sens..
[67] Quoc V. Le,et al. DropBlock: A regularization method for convolutional networks , 2018, NeurIPS.
[68] Geoffrey E. Hinton,et al. Dynamic Routing Between Capsules , 2017, NIPS.
[69] Patrick Hostert,et al. The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation , 2015, Remote. Sens..
[70] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[71] David A. Landgrebe,et al. Hyperspectral image data analysis , 2002, IEEE Signal Process. Mag..
[72] Erhan TURAN,et al. Local Binary Patterns and Extreme Learning Machine based Texture Classification of Marbles , 2018, 2018 International Conference on Artificial Intelligence and Data Processing (IDAP).
[73] Okan K. Ersoy,et al. Ladder Networks for Semi-Supervised Hyperspectral Image Classification , 2018, ArXiv.
[74] Stephen H. Savage,et al. Prospects and problems in the use of hyperspectral imagery for archaeological remote sensing: a case study from the Faynan copper mining district, Jordan , 2012 .
[75] Pierre Defourny,et al. Survey of current hyperspectral Earth observation applications from space and synergies with Sentinel-2 , 2017, 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp).
[76] Sergey Ioffe,et al. Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models , 2017, NIPS.
[77] Derek Anderson,et al. Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community , 2017 .
[78] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[79] Xudong Kang,et al. Dual-Path Network-Based Hyperspectral Image Classification , 2019, IEEE Geoscience and Remote Sensing Letters.
[80] Lin Zhu,et al. Generative Adversarial Networks for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[81] Xiaoou Tang,et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net , 2018, ECCV.
[82] Wei Li,et al. 3-D Convolution-Recurrent Networks for Spectral-Spatial Classification of Hyperspectral Images , 2019, Remote. Sens..
[83] Yong Wang,et al. Multi-manifold Clustering , 2010, PRICAI.
[84] Dmitry P. Vetrov,et al. Variational Dropout Sparsifies Deep Neural Networks , 2017, ICML.
[85] Shanjun Mao,et al. Spectral–spatial classification of hyperspectral images using deep convolutional neural networks , 2015 .
[86] Francis R. Bach,et al. Breaking the Curse of Dimensionality with Convex Neural Networks , 2014, J. Mach. Learn. Res..
[87] Eyal Ben Dor,et al. SHALOM – A Commercial Hyperspectral Space Mission , 2015 .
[88] Li He,et al. Canopy Vegetation Indices from In situ Hyperspectral Data to Assess Plant Water Status of Winter Wheat under Powdery Mildew Stress , 2017, Front. Plant Sci..
[89] Yoshua Bengio,et al. Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.
[90] Jeremias Penttilä,et al. A method for anomaly detection in hyperspectral images, using deep convolutional autoencoders , 2017 .
[91] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[92] B. Xu,et al. Noise estimation in a noise-adjusted principal component transformation and hyperspectral image restoration , 2008, Canadian Journal of Remote Sensing.
[93] Claude Cariou,et al. Unsupervised Nearest Neighbors Clustering With Application to Hyperspectral Images , 2015, IEEE Journal of Selected Topics in Signal Processing.
[94] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[95] E. M. Winter,et al. Anomaly detection from hyperspectral imagery , 2002, IEEE Signal Process. Mag..
[96] Haowen Luo,et al. Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification , 2018, ArXiv.
[97] James V. Taranik,et al. Mapping rock forming minerals at Boundary Canyon, Death Valey National Park, California, using aerial SEBASS thermal infrared hyperspectral image data , 2018, Int. J. Appl. Earth Obs. Geoinformation.
[98] D. Smart,et al. Evaluation of Hyperspectral Reflectance Indexes to Detect Grapevine Water Status in Vineyards , 2007, American Journal of Enology and Viticulture.
[99] Gang Wang,et al. Convolutional recurrent neural networks: Learning spatial dependencies for image representation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[100] Stefania Matteoli,et al. The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.
[101] David Krutz,et al. DESIS (DLR Earth Sensing Imaging Spectrometer for the ISS-MUSES platform) , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[102] Antonio J. Plaza,et al. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[103] Lin Zhu,et al. Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network , 2017, IEEE Geoscience and Remote Sensing Letters.
[104] John R. Miller,et al. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .
[105] Jonathan Tompson,et al. Efficient object localization using Convolutional Networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[106] Savita P. Sabale,et al. Supervised, Unsupervised, and Semisupervised Classification Methods for Hyperspectral Image Classification-A Review , 2014 .
[107] Richard J. Murphy,et al. Pretraining for Hyperspectral Convolutional Neural Network Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[108] Jinming Du,et al. A Hyperspectral Target Detection Framework With Subtraction Pixel Pair Features , 2018, IEEE Access.
[109] Savita P. Sabale,et al. Hyperspectral Image Classification Methods in Remote Sensing - A Review , 2015, 2015 International Conference on Computing Communication Control and Automation.
[110] L. Fiumi,et al. Evaluation of remote sensing data for urban planning. Applicative examples by means of multispectral and hyperspectral data , 2003, 2003 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas.
[111] Jia Deng,et al. Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.
[112] Liangpei Zhang,et al. An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery , 2013, IEEE Transactions on Geoscience and Remote Sensing.
[113] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[114] Qingshan Liu,et al. Cascaded Recurrent Neural Networks for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[115] Jun Zhou,et al. On the Sampling Strategy for Evaluation of Spectral-Spatial Methods in Hyperspectral Image Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[116] Antonio J. Plaza,et al. A New Cloud Computing Architecture for the Classification of Remote Sensing Data , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[117] Arto Kaarna,et al. Sequential spectral clustering of hyperspectral remote sensing image over bipartite graph , 2018, Appl. Soft Comput..
[118] Yushi Chen,et al. Optimized Input for CNN-Based Hyperspectral Image Classification Using Spatial Transformer Network , 2019, IEEE Geoscience and Remote Sensing Letters.
[119] Qiang Zhang,et al. Hyperspectral Image Denoising Employing a Spatial–Spectral Deep Residual Convolutional Neural Network , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[120] Françoise Bellegarde,et al. Transformation Ordering , 1987, TAPSOFT, Vol.1.
[121] Fang Tang,et al. Deep Learning With Grouped Features for Spatial Spectral Classification of Hyperspectral Images , 2017, IEEE Geoscience and Remote Sensing Letters.
[122] Yansheng Li,et al. Unsupervised Spectral–Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification , 2015, IEEE Geoscience and Remote Sensing Letters.
[123] Alberto Signoroni,et al. Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review , 2019, J. Imaging.
[124] Robert Mullins,et al. On the Reduction of Computational Complexity of Deep Convolutional Neural Networks † , 2018, Entropy.
[125] Elizabeth Pattey,et al. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance , 2002 .
[126] David J. Field,et al. Wavelets, vision and the statistics of natural scenes , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[127] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[128] Qian Du,et al. Learning Sensor-Specific Spatial-Spectral Features of Hyperspectral Images via Convolutional Neural Networks , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[129] Heesung Kwon,et al. Contextual deep CNN based hyperspectral classification , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[130] Naoto Yokoya,et al. Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art , 2017, IEEE Geoscience and Remote Sensing Magazine.
[131] Hyo-Eun Kim,et al. Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks , 2018, NeurIPS.
[132] John Shepanski,et al. Hyperion, a space-based imaging spectrometer , 2003, IEEE Trans. Geosci. Remote. Sens..
[133] Fang Liu,et al. Deep Multiscale Spectral-Spatial Feature Fusion for Hyperspectral Images Classification , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[134] Yoshua Bengio,et al. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.
[135] Josée Lévesque,et al. A demonstration of hyperspectral image exploitation for military applications , 2007, 2007 10th International Conference on Information Fusion.
[136] Qian Du,et al. Classification of Hyperspectral Imagery Using a New Fully Convolutional Neural Network , 2018, IEEE Geoscience and Remote Sensing Letters.
[137] Antonio J. Plaza,et al. Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification , 2018, Remote. Sens..
[138] Petia Radeva,et al. Meta-Parameter Free Unsupervised Sparse Feature Learning , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[139] Qian Du,et al. A survey on representation-based classification and detection in hyperspectral remote sensing imagery , 2016, Pattern Recognit. Lett..
[140] Xiaoqiang Lu,et al. Remote Sensing Image Scene Classification: Benchmark and State of the Art , 2017, Proceedings of the IEEE.
[141] Pascal Vincent,et al. Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives , 2012, ArXiv.
[142] Jun Li,et al. Unsupervised Feature Extraction in Hyperspectral Images Based on Wasserstein Generative Adversarial Network , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[143] Pedram Ghamisi,et al. Spectral and Spatial Classification of Hyperspectral Data , 2015 .
[144] Yang Hong,et al. A back - propagation neural network for mineralogical mapping from AVIRIS data , 1997 .
[145] Jie Yang,et al. Convolutional neural network in network (CNNiN): hyperspectral image classification and dimensionality reduction , 2019, IET Image Process..
[146] Shuigeng Zhou,et al. DeepCluster: A General Clustering Framework Based on Deep Learning , 2017, ECML/PKDD.
[147] Pushparaja Murugan. Feed Forward and Backward Run in Deep Convolution Neural Network , 2017, ArXiv.
[148] Berrin A. Yanikoglu,et al. Deep Learning With Attribute Profiles for Hyperspectral Image Classification , 2016, IEEE Geoscience and Remote Sensing Letters.
[149] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[150] Hui Lin,et al. Classification of Hyperspectral Images by Gabor Filtering Based Deep Network , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[151] Xiao Xiang Zhu,et al. Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources , 2017, IEEE Geoscience and Remote Sensing Magazine.
[152] Qian Du,et al. Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[153] Mercedes Eugenia Paoletti,et al. Visual Attention-Driven Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[154] D. L. Evans,et al. Recent Advances In Airborne Terrestrial Remote Sensing With The Nasa Airborne Visible/infrared Imaging Spectrometer (aviris), Airborne Synthetic Aperture Radar (sar), And Thermal Infrared Multispectral Scanner (tims) , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.
[155] Antonio Plaza,et al. Low–High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification , 2019, IEEE Geoscience and Remote Sensing Letters.
[156] Liangpei Zhang,et al. On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.
[157] Aleksander Madry,et al. How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift) , 2018, NeurIPS.
[158] Qian Du,et al. A linear constrained distance-based discriminant analysis for hyperspectral image classification , 2001, Pattern Recognit..
[159] Heather McNairn,et al. Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data , 2006 .
[160] Qihao Weng,et al. A survey of image classification methods and techniques for improving classification performance , 2007 .
[161] Y. El-Sharkawy,et al. Hyperspectral imaging: Anew prospective for remote recognition of explosive materials , 2019, Remote Sensing Applications: Society and Environment.
[162] Haokui Zhang,et al. Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network , 2017 .
[163] Yoshua Bengio,et al. Série Scientifique Scientific Series Incorporating Second-order Functional Knowledge for Better Option Pricing Incorporating Second-order Functional Knowledge for Better Option Pricing , 2022 .
[164] Jonathan Cheung-Wai Chan,et al. Learning and Transferring Deep Joint Spectral–Spatial Features for Hyperspectral Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[165] Zhiming Luo,et al. Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[166] R. Jenssen,et al. 1 THE HYMAP TM AIRBORNE HYPERSPECTRAL SENSOR : THE SYSTEM , CALIBRATION AND PERFORMANCE , 1998 .
[167] Mustafa Teke,et al. A short survey of hyperspectral remote sensing applications in agriculture , 2013, 2013 6th International Conference on Recent Advances in Space Technologies (RAST).
[168] Yoshua Bengio,et al. Deconstructing the Ladder Network Architecture , 2015, ICML.
[169] Ting Yuan,et al. Hyperspectral Image Classification with Capsule Network Using Limited Training Samples , 2018, Sensors.
[170] Jason Weston,et al. Semisupervised Neural Networks for Efficient Hyperspectral Image Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.
[171] Gustavo Camps-Valls,et al. Recent advances in remote sensing image processing , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).
[172] Chen Li,et al. Spatial Sequential Recurrent Neural Network for Hyperspectral Image Classification , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[173] A. Mookambiga,et al. Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery , 2016, Multidimens. Syst. Signal Process..
[174] Ronald L. Rivest,et al. Training a 3-node neural network is NP-complete , 1988, COLT '88.
[175] Xing Chen,et al. Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images , 2016, J. Sensors.
[176] Jun Zhou,et al. On the sampling strategies for evaluation of joint spectral-spatial information based classifiers , 2015, 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).
[177] Jonathan Cheung-Wai Chan,et al. Hyperspectral image classification using two-channel deep convolutional neural network , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[178] Serge J. Belongie,et al. Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[179] Hao Wu,et al. Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification , 2018, IEEE Transactions on Image Processing.
[180] Chenming Li,et al. Convolution Neural Network Based on Two-Dimensional Spectrum for Hyperspectral Image Classification , 2018, J. Sensors.
[181] Antonio J. Plaza,et al. Recent Developments in High Performance Computing for Remote Sensing: A Review , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[182] Andrea Vedaldi,et al. Understanding deep image representations by inverting them , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[183] C. Ananasso,et al. THE PRISMA HYPERSPECTRAL MISSION , 2014 .
[184] Kim-Kwang Raymond Choo,et al. Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification , 2016, Soft Computing.
[185] Jonathan Cheung-Wai Chan,et al. Hyperspectral Image Super-Resolution Based on Spatial and Spectral Correlation Fusion , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[186] Kurt Hornik,et al. Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.
[187] Susan L. Ustin,et al. Evaluation of the potential of Hyperion for fire danger assessment by comparison to the Airborne Visible/Infrared Imaging Spectrometer , 2003, IEEE Trans. Geosci. Remote. Sens..
[188] Adil Mehmood Khan,et al. Hyperspectral Band Selection Using Unsupervised Non-Linear Deep Auto Encoder to Train External Classifiers , 2017, Optik.
[189] J. Aravinth,et al. Big data challenges in airborne hyperspectral image for urban landuse classification , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).
[190] Qingshan Liu,et al. Integrating Convolutional Neural Network and Gated Recurrent Unit for Hyperspectral Image Spectral-Spatial Classification , 2018, PRCV.
[191] Carla P. Gomes,et al. Understanding Batch Normalization , 2018, NeurIPS.
[192] Zhuowen Tu,et al. Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[193] Kaiming He,et al. Group Normalization , 2018, ECCV.
[194] Pierre Defourny,et al. Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context , 2018, Remote. Sens..
[195] Tao Li,et al. DCNR: deep cube CNN with random forest for hyperspectral image classification , 2018, Multimedia Tools and Applications.
[196] Ning Wang,et al. Estimation of precipitable water from the thermal infrared hyperspectral data , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.
[197] Helmi Zulhaidi Mohd Shafri,et al. The Performance of Maximum Likelihood, Spectral Angle Mapper, Neural Network and Decision Tree Classifiers in Hyperspectral Image Analysis , 2007 .
[198] Chenghai Yang,et al. Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability , 2004, Precision Agriculture.
[199] Seniha Esen Yuksel,et al. Hyperspectral data classification using deep convolutional neural networks , 2016, 2016 24th Signal Processing and Communication Application Conference (SIU).
[200] Kenli Li,et al. Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[201] Jun Li,et al. Advanced Spectral Classifiers for Hyperspectral Images: A review , 2017, IEEE Geoscience and Remote Sensing Magazine.
[202] Zhen Li,et al. Towards Better Analysis of Deep Convolutional Neural Networks , 2016, IEEE Transactions on Visualization and Computer Graphics.
[203] Simon J. Hook,et al. Synergies Between VSWIR and TIR Data for the Urban Environment: An Evaluation of the Potential for the Hyperspectral Infrared Imager (HyspIRI) , 2012 .
[204] Tim Salimans,et al. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks , 2016, NIPS.
[205] Liang-pei Zhang,et al. Spectral-spatial classification of hyperspectral imagery with cooperative game , 2018 .
[206] Filiberto Pla,et al. A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[207] Antonio J. Plaza,et al. Fast dimensionality reduction and classification of hyperspectral images with extreme learning machines , 2018, Journal of Real-Time Image Processing.
[208] Bo Du,et al. Hyperspectral image classification via a random patches network , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[209] Hongliang Zhu,et al. Survey of supervised classification techniques for hyperspectral images , 2017 .
[210] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[211] N. Keshava,et al. Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[212] Jan Eric Lenssen,et al. Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.
[213] I. El-Magd,et al. Quantitative hyperspectral analysis for characterization of the coastal water from Damietta to Port Said, Egypt , 2014 .
[214] Robert Li,et al. Wavelet Pooling for Convolutional Neural Networks , 2018, ICLR.
[215] Yiwen Wang,et al. Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification , 2017, Remote. Sens..
[216] Jon Atli Benediktsson,et al. Deep Learning for Hyperspectral Image Classification: An Overview , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[217] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[218] Ying Li,et al. Convolutional Neural Networks Based Hyperspectral Image Classification Method with Adaptive Kernels , 2017, Remote. Sens..
[219] Qian Du,et al. Hyperspectral Image Classification Using Deep Pixel-Pair Features , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[220] Samy Bengio,et al. Links between perceptrons, MLPs and SVMs , 2004, ICML.
[221] Lorenzo Bruzzone,et al. Two-Stream Deep Architecture for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[222] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[223] Antonio Plaza,et al. Cloud Deep Networks for Hyperspectral Image Analysis , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[224] Michael T. Eismann,et al. Hyperspectral Image Classification , 2012 .
[225] K. P. Soman,et al. Dimensionally Reduced Features for Hyperspectral Image Classification Using Deep Learning , 2018, ICC 2018.
[226] Yang Li,et al. Self-Supervised Feature Learning With CRF Embedding for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[227] Nojun Kwak,et al. Analysis on the Dropout Effect in Convolutional Neural Networks , 2016, ACCV.
[228] Paul D. Gader,et al. A Review of Nonlinear Hyperspectral Unmixing Methods , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[229] D. Nagesh Kumar,et al. Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach , 2018 .
[230] Bing Liu,et al. A semi-supervised convolutional neural network for hyperspectral image classification , 2017 .
[231] Pushparaja Murugan,et al. Regularization and Optimization strategies in Deep Convolutional Neural Network , 2017, ArXiv.
[232] Ying Wang,et al. Deep Learning Ensemble for Hyperspectral Image Classification , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[233] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[234] Nicolas Courty,et al. Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions , 2015, ArXiv.
[235] Shutao Li,et al. Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[236] Peijun Du,et al. Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging , 2016, Neurocomputing.
[237] Junwei Han,et al. Learning Compact and Discriminative Stacked Autoencoder for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[238] Raymond F. Kokaly,et al. Mineral information at micron to kilometer scales: Laboratory, field, and remote sensing imaging spectrometer data from the orange hill porphyry copper deposit, Alaska, USA , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[239] Kouichi Sakurai,et al. One Pixel Attack for Fooling Deep Neural Networks , 2017, IEEE Transactions on Evolutionary Computation.
[240] Venkatesan Muthukumar,et al. A Deep Belief Network for Classifying Remotely-Sensed Hyperspectral Data , 2015, ISVC.
[241] Leland McInnes,et al. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.
[242] Shuyuan Yang,et al. Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[243] J. Benediktsson,et al. New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning , 2018, IEEE Geoscience and Remote Sensing Magazine.
[244] Heesung Kwon,et al. Going Deeper With Contextual CNN for Hyperspectral Image Classification , 2016, IEEE Transactions on Image Processing.
[245] C. Sanz. DER (Dynamic Evidential Reasoning), applied to the classification of hyperspectral images , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).
[246] Yunsong Li,et al. Hyperspectral image reconstruction by deep convolutional neural network for classification , 2017, Pattern Recognit..
[247] M. Siegel,et al. Hyperspectral classification via deep networks and superpixel segmentation , 2015 .
[248] Ying Li,et al. Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network , 2017, Remote. Sens..
[249] Jun Huang,et al. Spectral-Spatial Attention Networks for Hyperspectral Image Classification , 2019, Remote. Sens..
[250] Jürgen Schmidhuber,et al. Training Very Deep Networks , 2015, NIPS.
[251] Shutao Li,et al. Hyperspectral Image Classification With Deep Feature Fusion Network , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[252] Wei Wei,et al. A Hyperspectral Image Classification Framework with Spatial Pixel Pair Features , 2017, Sensors.
[253] Lei Zhang,et al. Structured Sparse Coding-Based Hyperspectral Imagery Denoising With Intracluster Filtering , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[254] Yunsong Li,et al. Deep convolutional networks with residual learning for accurate spectral-spatial denoising , 2018, Neurocomputing.
[255] Carlo Gatta,et al. Unsupervised Deep Feature Extraction for Remote Sensing Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[256] Shutao Li,et al. Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[257] Shanjun Mao,et al. A deep learning framework for hyperspectral image classification using spatial pyramid pooling , 2016 .
[258] Johannes R. Sveinsson,et al. Multisource remote sensing data classification based on consensus and pruning , 2003, IEEE Trans. Geosci. Remote. Sens..
[259] Chein-I Chang,et al. Unsupervised hyperspectral image classification , 2007, SPIE Organic Photonics + Electronics.
[260] Xiao Xiang Zhu,et al. Unsupervised Spectral–Spatial Feature Learning via Deep Residual Conv–Deconv Network for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[261] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[262] Chein-I. Chang. Hyperspectral Data Exploitation: Theory and Applications , 2007 .
[263] Okuda Masahiro,et al. Hyperspectral Image Restoration , 2018 .
[264] Jian Zhu,et al. Deformable Convolutional Neural Networks for Hyperspectral Image Classification , 2018, IEEE Geoscience and Remote Sensing Letters.
[265] Tao Li,et al. HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image , 2018, 2018 International Conference on Audio, Language and Image Processing (ICALIP).
[266] Le Wang,et al. Incorporating spatial information in spectral unmixing: A review , 2014 .
[267] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[268] Qingshan Liu,et al. Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification , 2017, Remote. Sens..
[269] Ronald G. Resmini,et al. Mineral mapping with HYperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, U.S.A. , 1997 .
[270] Olaf Hellwich,et al. Correct and still wrong: The relationship between sampling strategies and the estimation of the generalization error , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[271] Zhou Guo,et al. On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery , 2015 .
[272] Yue Wu,et al. Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification , 2019, Remote. Sens..
[273] Antonio Plaza,et al. A new deep convolutional neural network for fast hyperspectral image classification , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.
[274] Antonio J. Plaza,et al. Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs , 2012, Journal of Real-Time Image Processing.
[275] Xing Zhao,et al. Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[276] Digvir S. Jayas,et al. Hyperspectral imaging to classify and monitor quality of agricultural materials , 2015 .
[277] Jian Sun,et al. Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[278] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[279] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[280] G. F. Hughes,et al. On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.
[281] Huadong Guo,et al. Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification , 2015 .
[282] Antonio J. Plaza,et al. Clusters Versus FPGA for Parallel Processing of Hyperspectral Imagery , 2008, Int. J. High Perform. Comput. Appl..
[283] J. Benediktsson,et al. Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[284] Joydeep Ghosh,et al. Investigation of the random forest framework for classification of hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[285] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[286] Lorenzo Bruzzone,et al. Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[287] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[288] Qiang Liu,et al. A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture , 2018, IEEE Access.
[289] Samy Bengio,et al. Torch: a modular machine learning software library , 2002 .
[290] Lin Zhao,et al. Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery , 2016 .
[291] Ying Li,et al. Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[292] Stephen K. Babey,et al. A compact airborne spectrographic imager (CASI) , 1989 .
[293] Gustavo Camps-Valls,et al. Kernel spectral angle mapper , 2016 .
[294] Jon Atli Benediktsson,et al. Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[295] Xiao Xiang Zhu,et al. A Self-Improving Convolution Neural Network for the Classification of Hyperspectral Data , 2016, IEEE Geoscience and Remote Sensing Letters.
[296] Yufei Wang,et al. Convolutional Neural Network and Convex Optimization , 2014 .
[297] Seth A. Herd,et al. The Role of Competitive Inhibition and Top-Down Feedback in Binding during Object Recognition , 2012, Front. Psychology.
[298] Bo Du,et al. Recent advances in hyperspectral image processing , 2012, Geo spatial Inf. Sci..
[299] Bing Liu,et al. Supervised Deep Feature Extraction for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[300] Antonio J. Plaza,et al. Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[301] Xudong Kang,et al. Semi-supervised deep learning for hyperspectral image classification , 2019, Remote Sensing Letters.
[302] Yingli Tian,et al. Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[303] Pedram Ghamisi,et al. A Machine Learning Framework for Drill-Core Mineral Mapping Using Hyperspectral and High-Resolution Mineralogical Data Fusion , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[304] Sotiris B. Kotsiantis,et al. Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.
[305] Antonio J. Plaza,et al. Parallel and Distributed Dimensionality Reduction of Hyperspectral Data on Cloud Computing Architectures , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[306] Raymond Y. K. Lau,et al. Hyperspectral Image Classification With Deep Learning Models , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[307] Paulo E. Rauber,et al. Visualizing the Hidden Activity of Artificial Neural Networks , 2017, IEEE Transactions on Visualization and Computer Graphics.
[308] Surya Ganguli,et al. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization , 2014, NIPS.
[309] R. Venkatesan,et al. Hyperspectral Image Features Classification Using Deep Learning Recurrent Neural Networks , 2019, Journal of Medical Systems.
[310] Lamei Zhang,et al. Band Attention Convolutional Networks For Hyperspectral Image Classification , 2019, ArXiv.
[311] Jun Zhou,et al. Deep Residual Convolutional Neural Network for Hyperspectral Image Super-Resolution , 2017, ICIG.
[312] Yoshua Bengio,et al. Scaling learning algorithms towards AI , 2007 .
[313] Tao Li,et al. GCN: GPU-Based Cube CNN Framework for Hyperspectral Image Classification , 2017, 2017 46th International Conference on Parallel Processing (ICPP).
[314] Sen Jia,et al. Convolutional neural networks for hyperspectral image classification , 2017, Neurocomputing.
[315] André Große-Stoltenberg,et al. Evaluation of Continuous VNIR-SWIR Spectra versus Narrowband Hyperspectral Indices to Discriminate the Invasive Acacia longifolia within a Mediterranean Dune Ecosystem , 2016, Remote. Sens..
[316] Pascal Vincent,et al. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.
[317] Wenju Wang,et al. A Fast Dense Spectral-Spatial Convolution Network Framework for Hyperspectral Images Classification , 2018, Remote. Sens..
[318] Nikhil Ketkar,et al. Introduction to Keras , 2017 .
[319] Nikolaos Doulamis,et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[320] Cong Wang,et al. A multi-label Hyperspectral image classification method with deep learning features , 2016, ICIMCS.
[321] Thomas Esch,et al. Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey , 2011, Remote. Sens..
[322] Guangyi Chen,et al. Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[323] Mohamed Farah,et al. Hyperspectral imagery classification based on semi-supervised 3-D deep neural network and adaptive band selection , 2019, Expert Syst. Appl..
[324] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[325] Filiberto Pla,et al. Capsule Networks for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[326] Xiaorui Ma,et al. Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning , 2016 .
[327] Johannes R. Sveinsson,et al. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.
[328] Wu Liu,et al. Stacked sparse autoencoder in hyperspectral data classification using spectral-spatial, higher order statistics and multifractal spectrum features , 2017 .
[329] Morris Riedel,et al. The Influence of Sampling Methods on Pixel-Wise Hyperspectral Image Classification with 3D Convolutional Neural Networks , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.
[330] Saiprasad Koturwar,et al. Weight Initialization of Deep Neural Networks(DNNs) using Data Statistics , 2017, ArXiv.
[331] Dan Hu,et al. Semisupervised Hyperspectral Image Classification Based on Generative Adversarial Networks , 2018, IEEE Geoscience and Remote Sensing Letters.
[332] Lawrence Carin,et al. Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks , 2015, AAAI.
[334] D. Roberts,et al. Hyperspectral remote sensing of fire: State-of-the-art and future perspectives , 2018, Remote Sensing of Environment.
[335] Jonathan Cheung-Wai Chan,et al. Hyperspectral Images Classification Based on Dense Convolutional Networks with Spectral-Wise Attention Mechanism , 2019, Remote. Sens..
[336] Nilanjan Dey,et al. A survey of image classification methods and techniques , 2014, 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT).
[337] D. Manea,et al. Comparison of spectral angle mapper and support vector machine classification methods for mapping skin burn using hyperspectral imaging , 2018, Photonics Europe.
[338] Chein-I Chang,et al. Anomaly detection in hyperspectral imagery , 2001, SPIE Defense + Commercial Sensing.
[339] Chunhui Zhao,et al. Spectral-Spatial Classification of Hyperspectral Imagery Based on Stacked Sparse Autoencoder and Random Forest , 2017 .
[340] Jun Li,et al. Hyperspectral Image Classification Using Random Occlusion Data Augmentation , 2019, IEEE Geoscience and Remote Sensing Letters.
[341] Xiao Xiang Zhu,et al. Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[342] Han Jiang,et al. Deep residual networks for hyperspectral image classification , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[343] Yi Shen,et al. Convolutional neural network based classification for hyperspectral data , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[344] Arko Lucieer,et al. Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest , 2019, Entropy.
[345] Vijay Lulla,et al. Hyperspectral Applications in Urban Geography , 2009 .
[346] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[347] Andrew J. R. Simpson. Dither is Better than Dropout for Regularising Deep Neural Networks , 2015, ArXiv.
[348] Liangpei Zhang,et al. Hyperspectral Image Restoration Using Low-Rank Matrix Recovery , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[349] Jean Ponce,et al. A Theoretical Analysis of Feature Pooling in Visual Recognition , 2010, ICML.
[350] Russell C. Hardie,et al. Hyperspectral resolution enhancement using high-resolution multispectral imagery with arbitrary response functions , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[351] Elena Marchiori,et al. Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting , 2018, Remote. Sens..
[352] Laurie A. Chisholm,et al. Classification of Australian Native Forest Species Using Hyperspectral Remote Sensing and Machine-Learning Classification Algorithms , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[353] Ting Liu,et al. Recent advances in convolutional neural networks , 2015, Pattern Recognit..
[354] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[355] Swarnajyoti Patra,et al. An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images , 2018 .
[356] J. Chanussot,et al. Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.
[357] Elad Plaut,et al. From Principal Subspaces to Principal Components with Linear Autoencoders , 2018, ArXiv.
[358] R. Richter,et al. Hyperspectral Sensors for Military Applications , 2005 .
[359] Jun Li,et al. Discriminative Low-Rank Gabor Filtering for Spectral–Spatial Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[360] Jefersson Alex dos Santos,et al. Towards better exploiting convolutional neural networks for remote sensing scene classification , 2016, Pattern Recognit..
[361] Barbara Caputo,et al. Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[362] David Bergström,et al. Hyperspectral image analysis using deep learning — A review , 2016, 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA).
[363] Bo-Cai Gao,et al. Portable Remote Imaging Spectrometer coastal ocean sensor: design, characteristics, and first flight results. , 2014, Applied optics.
[364] Jon Atli Benediktsson,et al. Advances in Spectral-Spatial Classification of Hyperspectral Images , 2013, Proceedings of the IEEE.
[365] Sunil Narumalani,et al. Detecting and Mapping Four Invasive Species Along The Floodplain of North Platte River, Nebraska , 2009, Weed Technology.
[366] Gang Wang,et al. Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[367] T. Younos,et al. Advances in Watershed Science and Assessment , 2015 .
[368] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[369] Juha Karhunen,et al. Chapter 7 – Unsupervised deep learning: A short review , 2015 .
[370] Brendan J. Frey,et al. Adaptive dropout for training deep neural networks , 2013, NIPS.
[371] R. Ablin,et al. A Survey of Hyperspectral Image Classification in Remote Sensing , 2013 .
[372] Priyanka Agrawal,et al. Machine Learning: A Review , 2020 .
[373] Steven Verstockt,et al. Hyperspectral Image Classification with Convolutional Neural Networks , 2015, ACM Multimedia.
[374] Xin Liu,et al. Classification based on deep convolutional neural networks with hyperspectral image , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[375] Richard Bellman,et al. Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.
[376] Xu Jianmin,et al. A new airborne Earth observing system and its applications , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).
[377] B. Kunkel,et al. ROSIS (Reflective Optics System Imaging Spectrometer) - A Candidate Instrument For Polar Platform Missions , 1988, Other Conferences.
[378] Nicolas Audebert,et al. Deep Learning for Classification of Hyperspectral Data: A Comparative Review , 2019, IEEE Geoscience and Remote Sensing Magazine.
[379] Yoshua Bengio,et al. Interpretable Convolutional Filters with SincNet , 2018, ArXiv.
[380] Antonio J. Plaza,et al. Parallel Hyperspectral Image and Signal Processing [Applications Corner] , 2011, IEEE Signal Processing Magazine.
[381] David R. Thompson,et al. Real-Time Atmospheric Correction of AVIRIS-NG Imagery , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[382] Ning Ma,et al. An Unsupervised Deep Hyperspectral Anomaly Detector , 2018, Sensors.
[383] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[384] Carlos Roberto de Souza Filho,et al. Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: Implications for onshore exploration and monitoring , 2017 .
[385] Andrea Sacchetti,et al. The PRISMA Program , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.
[386] Boris Ginsburg,et al. Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification , 2017, ArXiv.
[387] Rob Fergus,et al. Stochastic Pooling for Regularization of Deep Convolutional Neural Networks , 2013, ICLR.
[388] Paul Geladi,et al. Principal Component Analysis , 1987, Comprehensive Chemometrics.
[389] Gustavo Camps-Valls,et al. Composite kernels for hyperspectral image classification , 2006, IEEE Geoscience and Remote Sensing Letters.
[390] Hongying Liu,et al. An improved ISODATA algorithm for hyperspectral image classification , 2014, 2014 7th International Congress on Image and Signal Processing.
[391] Qian Du,et al. High Performance Computing for Hyperspectral Remote Sensing , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[392] Yann LeCun,et al. Regularization of Neural Networks using DropConnect , 2013, ICML.
[393] Jon Atli Benediktsson,et al. Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[394] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[395] Shihong Du,et al. Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[396] Rich Caruana,et al. Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping , 2000, NIPS.
[397] Wayne Luk,et al. Hardware Acceleration for Machine Learning , 2017, 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI).
[398] Jon Y. Hardeberg,et al. Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution , 2019, ArXiv.
[399] Jing Wang,et al. Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data , 2017, 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC).
[400] emontmej,et al. High Performance Computing , 2003, Lecture Notes in Computer Science.
[401] Hamid Saeed Khan,et al. Modern Trends in Hyperspectral Image Analysis: A Review , 2018, IEEE Access.
[402] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[403] Lin Huang,et al. A Multiscale Deep Middle-level Feature Fusion Network for Hyperspectral Classification , 2019, Remote. Sens..
[404] Geoffrey E. Hinton,et al. Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.
[405] Bo Du,et al. Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art , 2016, IEEE Geoscience and Remote Sensing Magazine.
[406] Qian Du,et al. Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[407] Michael S. Lew,et al. Deep learning for visual understanding: A review , 2016, Neurocomputing.
[408] R. Kavitha,et al. Hyperspectral image analysis techniques on remote sensing , 2017, 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS).
[409] Sabine Chabrillat,et al. Potential of hyperspectral imagery for the spatial assessment of soil erosion stages in agricultural semi-arid Spain at different scales , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.
[410] Jun Li,et al. Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[411] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[412] Zhihua Wei,et al. Mixed Pooling for Convolutional Neural Networks , 2014, RSKT.
[413] Hao Wu,et al. Convolutional Recurrent Neural Networks forHyperspectral Data Classification , 2017, Remote. Sens..
[414] Jon Atli Benediktsson,et al. Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.
[415] Lichao Mou,et al. Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection , 2016, Remote. Sens..
[416] Cheng Shi,et al. Multi-scale hierarchical recurrent neural networks for hyperspectral image classification , 2018, Neurocomputing.
[417] Matthias Hein,et al. Optimization Landscape and Expressivity of Deep CNNs , 2017, ICML.
[418] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[419] Shiming Xiang,et al. Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks , 2014, IEEE Geoscience and Remote Sensing Letters.
[420] Xiuping Jia,et al. Hyperspectral Image Classification Using Convolutional Neural Networks and Multiple Feature Learning , 2018, Remote. Sens..
[421] Jia Liu,et al. Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images , 2016 .
[422] Nicholas C. Coops,et al. Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data , 2003, IEEE Trans. Geosci. Remote. Sens..
[423] François Chollet,et al. Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[424] Jon Atli Benediktsson,et al. Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods , 2013, IEEE Signal Processing Magazine.
[425] David A. Landgrebe,et al. Signal Theory Methods in Multispectral Remote Sensing , 2003 .
[426] Jon Atli Benediktsson,et al. Morphological Attribute Profiles for the Analysis of Very High Resolution Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.
[427] Pierre Baldi,et al. Learning Activation Functions to Improve Deep Neural Networks , 2014, ICLR.
[428] Antonio J. Plaza,et al. Cloud implementation of the K-means algorithm for hyperspectral image analysis , 2016, The Journal of Supercomputing.
[429] Seong G. Kong,et al. Joint Hyperspectral Superresolution and Unmixing With Interactive Feedback , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[430] Emmanuel Arzuaga-Cruz,et al. Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[431] Antonio J. Plaza,et al. Fusion of hyperspectral and LiDAR data using morphological component analysis , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[432] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[433] Jon Atli Benediktsson,et al. SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images , 2010, IEEE Geoscience and Remote Sensing Letters.
[434] Fahad Shahbaz Khan,et al. Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification , 2017, ArXiv.
[435] Jon Atli Benediktsson,et al. Recent Advances in Techniques for Hyperspectral Image Processing , 2009 .
[436] Qian Du,et al. A Parallel Gaussian–Bernoulli Restricted Boltzmann Machine for Mining Area Classification With Hyperspectral Imagery , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[437] Fan Zhang,et al. Hierarchical feature learning with dropout k-means for hyperspectral image classification , 2016, Neurocomputing.
[438] Jonathan Cheung-Wai Chan,et al. Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection , 2018, Remote. Sens..
[439] Li-Rong Dai,et al. A Regression Approach to Speech Enhancement Based on Deep Neural Networks , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[440] Sven Behnke,et al. Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.
[441] Marco Körner,et al. Temporal Vegetation Modelling Using Long Short-Term Memory Networks for Crop Identification from Medium-Resolution Multi-spectral Satellite Images , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[442] Sampsa Koponen,et al. Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data , 2002 .
[443] Jie Wang,et al. Hyperspectral image classification with small training set by deep network and relative distance prior , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).