Multiclass Non-Randomized Spectral–Spatial Active Learning for Hyperspectral Image Classification

[1]  Xia Xu,et al.  Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Adil Mehmood Khan,et al.  Graph-based spatial-spectral feature learning for hyperspectral image classification , 2017, IET Image Process..

[3]  Yao Yu,et al.  Ensemble Learning for Hyperspectral Image Classification Using Tangent Collaborative Representation , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Francesca Bovolo,et al.  A Novel Technique for Subpixel Image Classification Based on Support Vector Machine , 2010, IEEE Transactions on Image Processing.

[5]  Saurabh Prasad,et al.  A Multiscale Deep Learning Approach for High-Resolution Hyperspectral Image Classification , 2021, IEEE Geoscience and Remote Sensing Letters.

[6]  Changyin Sun,et al.  AL-ELM: One uncertainty-based active learning algorithm using extreme learning machine , 2015, Neurocomputing.

[7]  Changyin Sun,et al.  Active Learning From Imbalanced Data: A Solution of Online Weighted Extreme Learning Machine , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Yi Wang,et al.  Multiple Kernel-Based SVM Classification of Hyperspectral Images by Combining Spectral, Spatial, and Semantic Information , 2020, Remote. Sens..

[9]  Adil Mehmood Khan,et al.  Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images , 2019, Remote. Sens..

[10]  Yu Jiang,et al.  Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities , 2020, Remote. Sens..

[11]  François Vincent,et al.  One-Step Generalized Likelihood Ratio Test for Subpixel Target Detection in Hyperspectral Imaging , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Lorenzo Bruzzone,et al.  Active and Semisupervised Learning for the Classification of Remote Sensing Images , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Nanjun He,et al.  Multiscale CNNs Ensemble Based Self-Learning for Hyperspectral Image Classification , 2020, IEEE Geoscience and Remote Sensing Letters.

[14]  D. Böhning Multinomial logistic regression algorithm , 1992 .

[15]  Cheng Deng,et al.  Graph based semi-supervised classification with probabilistic nearest neighbors , 2020, Pattern Recognit. Lett..

[16]  Saeid Homayouni,et al.  A GA-Based Multi-View, Multi-Learner Active Learning Framework for Hyperspectral Image Classification , 2020, Remote. Sens..

[17]  Settimo Termini,et al.  A Definition of a Nonprobabilistic Entropy in the Setting of Fuzzy Sets Theory , 1972, Inf. Control..

[18]  Wajahat Ali Khan,et al.  Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers , 2018, PloS one.

[19]  Mercedes Eugenia Paoletti,et al.  Inference in Supervised Spectral Classifiers for On-Board Hyperspectral Imaging: An Overview , 2020, Remote. Sens..

[20]  Kurt C. Lawrence,et al.  Essential processing methods of hyperspectral images of agricultural and food products , 2020 .

[21]  Patrick J. Cullen,et al.  UAV-hyperspectral imaging of spectrally complex environments , 2020 .

[22]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[23]  Antonio J. Plaza,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Spectral–Spatial Classification of Hyperspectral Data Usi , 2022 .

[24]  William J. Emery,et al.  Active Learning Methods for Remote Sensing Image Classification , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Sonia Chernova,et al.  Classification of Household Materials via Spectroscopy , 2018, IEEE Robotics and Automation Letters.

[26]  Lorenzo Bruzzone,et al.  Kernel-based methods for hyperspectral image classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Deyu Meng,et al.  Hyperspectral Image Classification With Convolutional Neural Network and Active Learning , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[28]  David A. Landgrebe,et al.  The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon , 1994, IEEE Trans. Geosci. Remote. Sens..

[29]  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.

[30]  Rui Zhang,et al.  Semi-Supervised Hyperspectral Image Classification Using Spatio-Spectral Laplacian Support Vector Machine , 2014, IEEE Geoscience and Remote Sensing Letters.

[31]  Yue Han,et al.  A Novel Object-Based Supervised Classification Method with Active Learning and Random Forest for PolSAR Imagery , 2018, Remote. Sens..

[32]  Antonio J. Plaza,et al.  Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Peijun Du,et al.  Hyperspectral Remote Sensing Image Classification Based on Rotation Forest , 2014, IEEE Geoscience and Remote Sensing Letters.

[34]  Zhi-Hua Zhou,et al.  Tri-training: exploiting unlabeled data using three classifiers , 2005, IEEE Transactions on Knowledge and Data Engineering.

[35]  Alper Koz,et al.  Ground-Based Hyperspectral Image Surveillance Systems for Explosive Detection: Part II—Radiance to Reflectance Conversions , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[36]  Manuel Mazzara,et al.  Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification , 2020 .

[37]  Bing Liu,et al.  Deep convolutional recurrent neural network with transfer learning for hyperspectral image classification , 2018, Journal of Applied Remote Sensing.