Abstract:The curse of dimensionality can be caused by directly using representation learning to classify hyperspectral image due to high dimensionality, high correlation between bands and limited samples of the hyperspectral image. For the hyperspectral image, not all spectral bands are available for specific classification tasks. Therefore, spatial aware collaborative representation based on augmented spatial spectral features network is proposed in this paper. A hierarchical spatial spectral features network is built according to the low dimensional manifolds inherent in the hyperspectral image. Features of high dimensional data are extracted by training network. Spatial aware collaborative representation algorithms are utilized for classification. Experiments on two hyperspectral remote sensing datasets, Indian Pines and Pavia University, verify the effectiveness of the proposed algorithm.
[1] JIA S, SHEN L L, ZHU J S, et al. A 3-D Gabor Phase-Based Co-ding and Matching Framework for Hyperspectral Imagery Classification. IEEE Transactions on Cybernetics, 2018, 48(4): 1176-1188. [2] OU X F, ZHANG Y M, WANG H P, et al. Hyperspectral Image Target Detection via Weighted Joint K-Nearest Neighbor and Multitask Learning Sparse Representation. IEEE Access, 2020, 8: 11503-11511. [3] SZABÓ L, BURAI P, DEÁK B, et al. Assessing the Efficiency of Multispectral Satellite and Airborne Hyperspectral Images for Land Cover Mapping in an Aquatic Environment with Emphasis on the Water Caltrop(Trapa Natans). International Journal of Remote Sensing, 2019, 40(13): 5192-5215. [4] QIN Q M, ZHANG Z L, CHEN L, et al. Oil and Gas Reservoir Exploration Based on Hyperspectral Remote Sensing and Super-Low-Frequency Electromagnetic Detection. Journal of Applied Remote Sensing, 2016, 10(1). DOI: 10.1117/1.JRS.10.016017. [5] XIANG P, ZHOU H X, LI H, et al. Hyperspectral Anomaly Detection by Local Joint Subspace Process and Support Vector Machine. International Journal of Remote Sensing, 2020, 41(10): 3798-3819. [6] SU H J, TIAN S F, CAI Y, et al. Optimized Extreme Learning Machine for Urban Land Cover Classification Using Hyperspectral Imagery. Frontiers of Earth Science, 2017, 11(4): 765-773. [7] ZHANG Y Q, CAO G, LI X S, et al. Cascaded Random Forest for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(4): 1082-1094. [8] TU B, ZHANG X F, WANG J P, et al. Noisy Labels Detection in Hyperspectral Image via Class-Dependent Collaborative Representation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(12): 5076-5085. [9] AYDEMIR M S, BILGIN G. Semi-supervised Sparse Representation Classifier(S3RC) with Deep Features on Small Sample Sized Hyperspectral Images. Neurocomputing, 2020, 399: 213-226. [10] YANG J H, QIAN J X. Joint Collaborative Representation with Shape Adaptive Region and Locally Adaptive Dictionary for Hyperspectral Image Classification. IEEE Geoscience and Remote Sen-sing Letters, 2020, 17(4): 671-675. [11] WRIGHT J, YANG A Y, GANESH A, et al. Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227. [12] CHEN Y, NASRABADI N M, TRAN T D. Hyperspectral Image Classification Using Dictionary-Based Sparse Representation. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3973-3985. [13] ZHANG L, YANG M, FENG X C. Sparse Representation or Co-llaborative Representation: Which Helps Face Recognition? // Proc of the International Conference on Computer Vision. Washington, USA: IEEE, 2011: 471-478. [14] LI W, DU Q. Joint Within-Class Collaborative Representation for Hyperspectral Image Classification. IEEE Journal of Selected To-pics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2200-2208. [15] XIONG M M, RAN Q, LI W, et al. Hyperspectral Image Classification Using Weighted Joint Collaborative Representation. IEEE Geoscience and Remote Sensing Letters, 2015, 12(6): 1209-1213. [16] SU H J, ZHAO B, DU Q, et al. Kernel Collaborative Representation with Local Correlation Features for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2): 1230-1241. [17] JIANG J J, CHEN C, YU Y, et al. Spatial-Aware Collaborative Representation for Hyperspectral Remote Sensing Image Classification. IEEE Geoence and Remote Sensing Letters, 2017, 14(3): 404-408. [18] YU H Y, GAO L R, LIAO W Z, et al. Global Spatial and Local Spectral Similarity-Based Manifold Learning Group Sparse Representation for Hyperspectral Imagery Classification. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(5): 3043-3056. [19] ZHANG H, LIU W J, LÜ H H. Spatial-Spectral Joint Classification of Hyperspectral Image with Locality and Edge Preserving. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 2240-2250. [20] ZHOU Y C, PENG J T, CHEN C L P. Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2): 1082-1095. [21] LI S T, SONG W W, FANG L Y, et al. Deep Learning for Hyperspectral Image Classification: An Overview. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6690-6709. [22] CHEN Y S, LIN Z H, ZHAO X, et al. Deep Learning-Based Cla-ssification of Hyperspectral Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2094-2107. [23] CHEN Y S, ZHAO X, JIA X P, et al. Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 2381-2392. [24] ZHAO W Z, DU S H. Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4544-4554. [25] CAO X Y, YAO J, XU Z B, et al. Hyperspectral Image Classification with Convolutional Neural Network and Active Learning. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4604-4616. [26] KANG X D, LI C C, LI S T, et al. Classification of Hyperspectral Images by Gabor Filtering Based Deep Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sen-sing, 2018, 11(4): 1166-1178. [27] XU Y H, DU B, ZHANG F, et al. Hyperspectral Image Classification via a Random Patches Network. ISPRS Journal of Photogra-mmetry and Remote Sensing, 2018, 142: 344-357. [28] ZHOU Y C, WEI Y T. Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification. IEEE Transactions on Cybernetics, 2016, 46(7): 1667-1678. [29] 王军浩,闫德勤,刘德山,等.高光谱图像分类的融合分层深度网络联合稀疏表示算法.模式识别与人工智能, 2020, 33(4): 303-312. (WANG J H, YAN D Q, LIU D S, et al. Joint Sparse Representation Fusing Hierarchical Deep Network of Hyperspectral Image Classification. Pattern Recognition and Artificial Intelligence, 2020, 33(4): 303-312.) [30] LI W, TRAMEL E W, PRASAD S, et al. Nearest Regularized Subspace for Hyperspectral Classification. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 477-489. [31] MU C, GUO Z, LIU Y. A Multi-scale and Multi-level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification. Remote Sensing, 2020, 12(1): 125-147. [32] TU B, KUANG W L, ZHAO G Z, et al. Hyperspectral Image Cla- ssification by Combining Local Binary Pattern and Joint Sparse Representation. International Journal of Remote Sensing, 2019, 40(24): 9484-9500. [33] LIU J J, WU Z B, XIAO Z Y, et al. Region-Based Relaxed Multiple Kernel Collaborative Representation for Hyperspectral Image Classification. IEEE Access, 2017, 5: 20921-20933. [34] GAO F, WANG Q, DONG J Y, et al. Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-graphs. Remote Sensing, 2018, 10(8): 1271-1291. [35] CHEN C, WEI L, SU H J, et al. Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine. Remote Sensing, 2014, 6(6): 5795-5814. [36] TU B, ZHANG X F, KANG X D, et al. Hyperspectral Image Classification via Fusing Correlation Coefficient and Joint Sparse Representation. IEEE Geoence and Remote Sensing Letters, 2018, 15(3): 340-344. [37] LI W, ZHANG Y X, LIU N, et al. Structure-Aware Collaborative Representation for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 7246-7261. [38] HE L, LI J, LIU C Y, et al. Recent Advances on Spectral-Spatial Hyperspectral Image Classification: An Overview and New Guidelines. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(3): 1579-1597.