Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization.

[1]  Bertrand Le Saux,et al.  Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks , 2016, ACCV.

[2]  Yong Du,et al.  Hierarchical recurrent neural network for skeleton based action recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Gui-Song Xia,et al.  Learning High-level Features for Satellite Image Classification With Limited Labeled Samples , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Sanja Fidler,et al.  HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Tong Zhang,et al.  Deep Learning Based Feature Selection for Remote Sensing Scene Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[6]  Igor Sevo,et al.  Convolutional Neural Network Based Automatic Object Detection on Aerial Images , 2016, IEEE Geoscience and Remote Sensing Letters.

[7]  Seunghoon Hong,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[8]  Arnt-Borre Salberg,et al.  Detection of seals in remote sensing images using features extracted from deep convolutional neural networks , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[9]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[10]  Yoshua Bengio,et al.  Practical Recommendations for Gradient-Based Training of Deep Architectures , 2012, Neural Networks: Tricks of the Trade.

[11]  Gui-Song Xia,et al.  SAR-Based Terrain Classification Using Weakly Supervised Hierarchical Markov Aspect Models , 2012, IEEE Transactions on Image Processing.

[12]  Simon A. Wagner,et al.  SAR ATR by a combination of convolutional neural network and support vector machines , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Gui-Song Xia,et al.  Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Brian P. Salmon,et al.  Multiview Deep Learning for Land-Use Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[15]  Hongliang Ren,et al.  Hierarchical Recognition System for Target Recognition from Sparse Representations , 2015 .

[16]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[17]  Uwe Stilla,et al.  Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks , 2016, IEEE Geoscience and Remote Sensing Letters.

[18]  Maoguo Gong,et al.  Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Jamie Sherrah,et al.  Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery , 2016, ArXiv.

[20]  Xin Yu,et al.  3D cost aggregation with multiple minimum spanning trees for stereo matching. , 2017, Applied optics.

[21]  Junwei Han,et al.  A Survey on Object Detection in Optical Remote Sensing Images , 2016, ArXiv.

[22]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[23]  Christian Cochin,et al.  Classification of ships using real and simulated data in a convolutional neural network , 2016, 2016 IEEE Radar Conference (RadarConf).

[24]  Pietro Perona,et al.  Cataloging Public Objects Using Aerial and Street-Level Images — Urban Trees , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Medeni Soysal,et al.  Performance Analysis of State-of-the-Art Representation Methods for Geographical Image Retrieval and Categorization , 2014, IEEE Geoscience and Remote Sensing Letters.

[26]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Roberto Cipolla,et al.  Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding , 2015, BMVC.

[28]  Fang Liu,et al.  SAR Image segmentation based on convolutional-wavelet neural network and markov random field , 2017, Pattern Recognit..

[29]  Zoubin Ghahramani,et al.  Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.

[30]  Yuqi Bai,et al.  A new research paradigm for global land cover mapping , 2016, Ann. GIS.

[31]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Matthieu Cord,et al.  WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[34]  Thomas Brox,et al.  Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT , 2014, ArXiv.

[35]  Bo Du,et al.  Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Xiaoqiang Lu,et al.  Remote Sensing Image Scene Classification: Benchmark and State of the Art , 2017, Proceedings of the IEEE.

[37]  Lei Guo,et al.  Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Qian Song,et al.  Zero-Shot Learning of SAR Target Feature Space With Deep Generative Neural Networks , 2017, IEEE Geoscience and Remote Sensing Letters.

[39]  Xintao Hu,et al.  Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping , 2016, Multidimens. Syst. Signal Process..

[40]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[41]  Uwe Stilla,et al.  Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection , 2016, ISPRS Journal of Photogrammetry and Remote Sensing.

[42]  B. S. Manjunath,et al.  Modeling and Detection of Geospatial Objects Using Texture Motifs , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Shawn D. Newsam,et al.  Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval , 2016, Remote. Sens..

[44]  David A. Clausi,et al.  Sea Ice Concentration Estimation During Melt From Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Eric R. Keydel,et al.  MSTAR extended operating conditions: a tutorial , 1996, Defense, Security, and Sensing.

[46]  Gui-Song Xia,et al.  Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery , 2016, Remote. Sens..

[47]  Jie Geng,et al.  High-Resolution SAR Image Classification via Deep Convolutional Autoencoders , 2015, IEEE Geoscience and Remote Sensing Letters.

[48]  Jun Wu,et al.  A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[49]  Mikhail F. Kanevski,et al.  Support-Based Implementation of Bayesian Data Fusion for Spatial Enhancement: Applications to ASTER Thermal Images , 2008, IEEE Geoscience and Remote Sensing Letters.

[50]  Junwei Han,et al.  Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[51]  Pierre Alliez,et al.  High-Resolution Semantic Labeling with Convolutional Neural Networks , 2016 .

[52]  Fachao Qin,et al.  Object-oriented ensemble classification for polarimetric SAR Imagery using restricted Boltzmann machines , 2017 .

[53]  Naoto Yokoya,et al.  Hyperspectral Pansharpening: A Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[54]  Luisa Verdoliva,et al.  Land Use Classification in Remote Sensing Images by Convolutional Neural Networks , 2015, ArXiv.

[55]  Jan Dirk Wegner,et al.  Toward Seamless Multiview Scene Analysis From Satellite to Street Level , 2017, Proceedings of the IEEE.

[56]  Jefersson Alex dos Santos,et al.  Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[57]  Qingshan Liu,et al.  Cascaded Recurrent Neural Networks for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[58]  Colin P. Schwegmann,et al.  Very deep learning for ship discrimination in Synthetic Aperture Radar imagery , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[59]  Yansheng Li,et al.  Unsupervised Spectral–Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[60]  Uwe Stilla,et al.  SEMANTIC SEGMENTATION OF AERIAL IMAGES WITH AN ENSEMBLE OF CNNS , 2016 .

[61]  Xiao Xiang Zhu,et al.  FusioNet: A two-stream convolutional neural network for urban scene classification using PolSAR and hyperspectral data , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[62]  Jie Geng,et al.  Deep Supervised and Contractive Neural Network for SAR Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[63]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[64]  Scott Workman,et al.  On the location dependence of convolutional neural network features , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[65]  Curt H. Davis,et al.  Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks , 2007, Image Vis. Comput..

[66]  Xiao Xiang Zhu,et al.  Fully conv-deconv network for unsupervised spectral-spatial feature extraction of hyperspectral imagery via residual learning , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[67]  Michele Volpi,et al.  Joint height estimation and semantic labeling of monocular aerial images with CNNS , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[68]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[69]  Rahul Sukthankar,et al.  MatchNet: Unifying feature and metric learning for patch-based matching , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[70]  Vincent Lepetit,et al.  LIFT: Learned Invariant Feature Transform , 2016, ECCV.

[71]  Grgoire Montavon,et al.  Neural Networks: Tricks of the Trade , 2012, Lecture Notes in Computer Science.

[72]  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.

[73]  Maxime Moreaud,et al.  Sparse Stereo Disparity Map Densification Using Hierarchical Image Segmentation , 2017, ISMM.

[74]  Xiao Xiang Zhu,et al.  A CNN for the identification of corresponding patches in SAR and optical imagery of urban scenes , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[75]  Wen Yang,et al.  STRUCTURAL HIGH-RESOLUTION SATELLITE IMAGE INDEXING , 2010 .

[76]  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).

[77]  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.

[78]  Michael Kampffmeyer,et al.  Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[79]  Marc Pollefeys,et al.  Joint 3D Scene Reconstruction and Class Segmentation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[80]  Fan Zhang,et al.  Deep Convolutional Neural Networks for Hyperspectral Image Classification , 2015, J. Sensors.

[81]  Junwei Han,et al.  Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding , 2014 .

[82]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[83]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[84]  Shawn D. Newsam,et al.  Geographic Image Retrieval Using Local Invariant Features , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[85]  Viorica Patraucean,et al.  gvnn: Neural Network Library for Geometric Computer Vision , 2016, ECCV Workshops.

[86]  Marius Leordeanu,et al.  Dual Local-Global Contextual Pathways for Recognition in Aerial Imagery , 2016, ArXiv.

[87]  Lichao Mou,et al.  Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection , 2016, Remote. Sens..

[88]  Pabitra Mitra,et al.  BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[89]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[90]  Nicolas Courty,et al.  Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions , 2015, ArXiv.

[91]  Xiangtao Zheng,et al.  Hyperspectral Image Superresolution by Transfer Learning , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[92]  Hongwei Liu,et al.  Convolutional Neural Network With Data Augmentation for SAR Target Recognition , 2016, IEEE Geoscience and Remote Sensing Letters.

[93]  Yong Dou,et al.  Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks , 2015, J. Sensors.

[94]  Björn Tings,et al.  Ship-Iceberg Discrimination with Convolutional Neural Networks in High Resolution SAR Images , 2016 .

[95]  Michele Volpi,et al.  Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[96]  Shiming Xiang,et al.  Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks , 2014, IEEE Geoscience and Remote Sensing Letters.

[97]  Xiaoou Tang,et al.  Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.

[98]  Naoto Yokoya,et al.  Object Detection Based on Sparse Representation and Hough Voting for Optical Remote Sensing Imagery , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[99]  Jia Liu,et al.  Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images , 2016 .

[100]  Hong Sun,et al.  Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[101]  Jon Atli Benediktsson,et al.  Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods , 2013, IEEE Signal Processing Magazine.

[102]  Haipeng Wang,et al.  SAR target recognition based on deep learning , 2014, 2014 International Conference on Data Science and Advanced Analytics (DSAA).

[103]  Heiko Hirschmüller,et al.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[104]  Tong Zhang,et al.  Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings , 2016, ICML.

[105]  Wei Li,et al.  Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery , 2017, IEEE Geoscience and Remote Sensing Letters.

[106]  Nikos Paragios,et al.  Simultaneous registration, segmentation and change detection from multisensor, multitemporal satellite image pairs , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[107]  Xinlei Chen,et al.  Never-Ending Learning , 2012, ECAI.

[108]  Yu Zhou,et al.  Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks , 2016, IEEE Geoscience and Remote Sensing Letters.

[109]  Xiaoling Chen,et al.  Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes , 2006 .

[110]  Ying Li,et al.  Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network , 2017, Remote. Sens..

[111]  Andrea Vedaldi,et al.  Learning Covariant Feature Detectors , 2016, ECCV Workshops.

[112]  Xiao Xiang Zhu,et al.  Data Fusion and Remote Sensing: An ever-growing relationship , 2016, IEEE Geoscience and Remote Sensing Magazine.

[113]  Jocelyn Chanussot,et al.  Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[114]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[115]  Ya-Qiu Jin,et al.  Polarimetric Scattering and SAR Information Retrieval: Jin/Polarimetric Scattering and SAR Information Retrieval , 2013 .

[116]  José F. Moreno,et al.  An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning , 2015, Remote. Sens..

[117]  Kyoung Mu Lee,et al.  Look Wider to Match Image Patches With Convolutional Neural Networks , 2017, IEEE Signal Processing Letters.

[118]  Yann LeCun,et al.  Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[119]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[120]  Jinying Zhong,et al.  Remote Sensing Image Fusion with Convolutional Neural Network , 2016 .

[121]  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.

[122]  Lorenzo Torresani,et al.  Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[123]  Xiao Xiang Zhu,et al.  Fusing Meter-Resolution 4-D InSAR Point Clouds and Optical Images for Semantic Urban Infrastructure Monitoring , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[124]  Alexandre Boulch,et al.  Benchmarking classification of earth-observation data: From learning explicit features to convolutional networks , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[125]  Dan Zhang,et al.  Stacked Sparse Autoencoder in PolSAR Data Classification Using Local Spatial Information , 2016, IEEE Geoscience and Remote Sensing Letters.

[126]  Baojun Zhao,et al.  Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[127]  Nikos Paragios,et al.  Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[128]  Gui-Song Xia,et al.  Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery , 2015, Remote. Sens..

[129]  Michele Volpi,et al.  Semantic segmentation of urban scenes by learning local class interactions , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[130]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.

[131]  Jefersson Alex dos Santos,et al.  Towards better exploiting convolutional neural networks for remote sensing scene classification , 2016, Pattern Recognit..

[132]  Liujuan Cao,et al.  Deep neural networks-based vehicle detection in satellite images , 2015, 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB).

[133]  James Hays,et al.  Localizing and Orienting Street Views Using Overhead Imagery , 2016, ECCV.

[134]  Qian Du,et al.  Hyperspectral Image Classification Using Deep Pixel-Pair Features , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[135]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[136]  Gui-Song Xia,et al.  Sketch-based aerial image retrieval , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[137]  Gabriele Moser,et al.  Multimodal Classification of Remote Sensing Images: A Review and Future Directions , 2015, Proceedings of the IEEE.

[138]  Shuang Wang,et al.  Multilayer feature learning for polarimetric synthetic radar data classification , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[139]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[140]  Scott Workman,et al.  Wide-Area Image Geolocalization with Aerial Reference Imagery , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[141]  Lorenzo Bruzzone,et al.  Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances , 2016, IEEE Geoscience and Remote Sensing Magazine.

[142]  Alberto Moreira,et al.  A Preliminary Study on SAR Advanced Information Retrieval and Scene Reconstruction , 2016, IEEE Geoscience and Remote Sensing Letters.

[143]  J. D. Wegner,et al.  Towards Integrated 3D Reconstruction and Semantic Interpretation of Urban Scenes , 2016 .

[144]  Carlo Gatta,et al.  Unsupervised Deep Feature Extraction for Remote Sensing Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[145]  Qixiang Ye,et al.  Orientation robust object detection in aerial images using deep convolutional neural network , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[146]  Biao Hou,et al.  Classification of Polarimetric SAR Images Using Multilayer Autoencoders and Superpixels , 2016, IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens..

[147]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[148]  Gui-Song Xia,et al.  Bag-of-Visual-Words Scene Classifier With Local and Global Features for High Spatial Resolution Remote Sensing Imagery , 2016, IEEE Geoscience and Remote Sensing Letters.

[149]  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.

[150]  Davide Cozzolino,et al.  Pansharpening by Convolutional Neural Networks , 2016, Remote. Sens..

[151]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[152]  Gui-Song Xia,et al.  AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[153]  Naveen Kumar,et al.  An Overview of Automatic Target Recognition Systems for Underwater Mine Classification , 2016 .

[154]  Alexandre Boulch,et al.  Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[155]  Xiao Xiang Zhu,et al.  A Self-Improving Convolution Neural Network for the Classification of Hyperspectral Data , 2016, IEEE Geoscience and Remote Sensing Letters.

[156]  Devis Tuia,et al.  Geospatial Correspondences for Multimodal Registration , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[157]  Chris Kreucher,et al.  Modern approaches in deep learning for SAR ATR , 2016, SPIE Defense + Security.

[158]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[159]  Paolo Napoletano,et al.  Visual descriptors for content-based retrieval of remote-sensing images , 2016, ArXiv.

[160]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[161]  Serge J. Belongie,et al.  Learning deep representations for ground-to-aerial geolocalization , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[162]  G. Camps-Valls,et al.  Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis , 2015 .

[163]  Jan Dirk Wegner,et al.  Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-resolution Model for Multi-class Volumetric Labeling , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[164]  Hongyi Liu,et al.  A New Pan-Sharpening Method With Deep Neural Networks , 2015, IEEE Geoscience and Remote Sensing Letters.

[165]  Shawn D. Newsam,et al.  Bag-of-visual-words and spatial extensions for land-use classification , 2010, GIS '10.

[166]  Bo Du,et al.  Scene Classification via a Gradient Boosting Random Convolutional Network Framework , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[167]  Nataliia Kussul,et al.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[168]  Jin Zhao,et al.  Discriminant deep belief network for high-resolution SAR image classification , 2017, Pattern Recognit..

[169]  Thomas Brox,et al.  A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[170]  David Morgan,et al.  Deep convolutional neural networks for ATR from SAR imagery , 2015, Defense + Security Symposium.

[171]  Jamie Sherrah,et al.  Semantic Labeling of Aerial and Satellite Imagery , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[172]  Gui-Song Xia,et al.  Extreme value theory-based calibration for the fusion of multiple features in high-resolution satellite scene classification , 2013 .

[173]  Yann LeCun,et al.  Computing the stereo matching cost with a convolutional neural network , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[174]  Bolei Zhou,et al.  Learning Deep Features for Scene Recognition using Places Database , 2014, NIPS.

[175]  Haipeng Wang,et al.  Target Classification Using the Deep Convolutional Networks for SAR Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[176]  Xiao Xiang Zhu,et al.  Deep Recurrent Neural Networks for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[177]  Ning Li,et al.  SAR ATR based on displacement- and rotation-insensitive CNN , 2016 .

[178]  Fang Liu,et al.  Wishart Deep Stacking Network for Fast POLSAR Image Classification , 2016, IEEE Transactions on Image Processing.

[179]  Rob Fergus,et al.  Depth Map Prediction from a Single Image using a Multi-Scale Deep Network , 2014, NIPS.