[1] Majozi N P, Salama M S, Bernard S et al. Remote sensing of euphotic depth in shallow tropical inland waters of Lake Naivasha using MERIS data[J]. Remote Sensing of Environment, 148, 178-189(2014).
[2] Li F Y, Huo H T, Li J et al. Hyperspectral image classification via multiple-feature-based improved sparse representation[J]. Acta Optica Sinica, 39, 0528004(2019).
[3] Yu C Y, Zhao M, Song M P et al. Hyperspectral image classification method based on targets constraint and spectral-spatial iteration[J]. Acta Optica Sinica, 38, 0628003(2018).
[4] Hou B H, Yao M L, Wang R et al. Spatial-spectral semi-supervised local discriminant analysis for hyperspectral image classification[J]. Acta Optica Sinica, 37, 0728002(2017).
[5] Wang L G, Zhao C H[M]. Processing techniques of hyperspectral imagery(2013).
[6] Dalla Mura M, Benediktsson J A, Waske B et al. Morphological attribute profiles for the analysis of very high resolution images[J]. IEEE Transactions on Geoscience and Remote Sensing, 48, 3747-3762(2010).
[7] Ghamisi P, Dalla Mura M, Benediktsson J A. A survey on spectral-spatial classification techniques based on attribute profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 2335-2353(2015).
[8] Dalla Mura M, Atli Benediktsson J, Waske B et al. Extended profiles with morphological attribute filters for the analysis of hyperspectral data[J]. International Journal of Remote Sensing, 31, 5975-5991(2010).
[9] Cavallaro G, Falco N, Dalla Mura M et al. Automatic attribute profiles[J]. IEEE Transactions on Image Processing, 26, 1859-1872(2017).
[10] Aptoula E, Ozdemir M C, Yanikoglu B. Deep learning with attribute profiles for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 13, 1970-1974(2016).
[11] Wang L B, Zhen H, Fang X Y et al. A unified two-parallel-branch deep neural network for joint gland contour and segmentation learning[J]. Future Generation Computer Systems, 100, 316-324(2019).
[12] Kim R, Kim G, Kim H et al. A method for optimizing deep learning object detection in edge computing[C], 1164-1167(2020).
[13] Chen Y S, Lin Z H, Zhao X et al. Deep learning-based classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2094-2107(2014).
[14] Mura M D, Benediktsson J A, Bruzzone L. Modeling structural information for building extraction with morphological attribute filters[J]. Proceedings of SPIE, 7477, 747703(2009).
[15] Zhang X R, Liang Y J, Li C et al. Recursive autoencoders-based unsupervised feature learning for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 14, 1928-1932(2017).
[16] Geng J, Wang H Y, Fan J C et al. Change detection of SAR images based on supervised contractive autoencoders and fuzzy clustering[C](2017).
[17] Vincent P, Larochelle H, Lajoie I et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 11, 3371-3408(2010).
[18] Lin Z H, Chen Y S, Zhao X et al. Spectral-spatial classification of hyperspectral image using autoencoders[C](2013).
[19] Sun X, Zhou F, Dong J Y et al. Encoding spectral and spatial context information for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 14, 2250-2254(2017).
[20] Ghamisi P, Benediktsson J A, Sveinsson J R. Automatic spectral-spatial classification framework based on attribute profiles and supervised feature extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 5771-5782(2014).
[21] Bhardwaj K, Patra S, Bruzzone L. Threshold-free attribute profile for classification of hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 7731-7742(2019).
[22] Cavallaro G, Dalla Mura M, Benediktsson J A et al. Extended self-dual attribute profiles for the classification of hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 12, 1690-1694(2015).
[23] Zhou P C, Han J W, Cheng G et al. Learning compact and discriminative stacked autoencoder for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 4823-4833(2019).
[24] Li W, Chen C, Su H J et al. Local binary patterns and extreme learning machine for hyperspectral imagery classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 3681-3693(2015).
[25] Waske B, van der Linden S, Benediktsson J A et al. Sensitivity of support vector machines to random feature selection in classification of hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 48, 2880-2889(2010).
[26] Hu W, Huang Y Y, Wei L et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 258619(2015).
[27] Jiao L C, Liang M M, Chen H et al. Deep fully convolutional network-based spatial distribution prediction for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 5585-5599(2017).
