[1] Bioucas-Dias J M, Plaza A, Camps-Valls G et al. Hyperspectral remote sensing data analysis and future challenges[J]. IEEE Geoscience and Remote Sensing Magazine, 1, 6-36(2013).
[2] Wang J G, Guan S. Research on development of hyper-spectral imaging satellite[J]. Electro-Optic Technology Application, 35, 1-7(2020).
[4] Tan K, Wang H M, Chen L H et al. Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest[J]. Journal of Hazardous Materials, 382, 120987(2020).
[5] Li W, Dou Z G, Cui L J et al. Suitability of hyperspectral data for monitoring nitrogen and phosphorus content in constructed wetlands[J]. Remote Sensing Letters, 11, 495-504(2020).
[6] Zhang Y S, Wu L, Ren H Z et al. Mapping water quality parameters in urban rivers from hyperspectral images using a new self-adapting selection of multiple artificial neural networks[J]. Remote Sensing, 12, 336(2020).
[7] Veraverbeke S, Dennison P, Gitas I et al. Hyperspectral remote sensing of fire: state-of-the-art and future perspectives[J]. Remote Sensing of Environment, 216, 105-121(2018).
[8] Li S T, Song W W, Fang L Y et al. Deep learning for hyperspectral image classification: an overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 6690-6709(2019).
[9] Luo F L, Zhang L P, Du B et al. Dimensionality reduction with enhanced hybrid-graph discriminant learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 5336-5353(2020).
[10] Sun L, Wu F Y, Zhan T M et al. Weighted nonlocal low-rank tensor decomposition method for sparse unmixing of hyperspectral images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1174-1188(2020).
[11] Vincent F, Besson O. One-step generalized likelihood ratio test for subpixel target detection in hyperspectral imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 4479-4489(2020).
[12] Zhang H K, Li Y, Jiang Y N. Deep learning for hyperspectral imagery classification: the state of the art and prospects[J]. Acta Automatica Sinica, 44, 961-977(2018).
[13] Lee J Y, Kim J S, Kim T Y et al. Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm[J]. Scientific Reports, 10, 20546(2020).
[14] Singh D, Kumar V, Vaishali et al. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks[J]. European Journal of Clinical Microbiology & Infectious Diseases, 39, 1379-1389(2020).
[15] Liu J M, Yang S, Huang H. Hyperspectral remote sensing image classification based on local reconstruction Fisher analysis[J]. Chinese Journal of Lasers, 47, 0710001(2020).
[16] Liu L X, He D, Li M Z et al. Identification of Xinjiang jujube varieties based on hyperspectral technique and machine learning[J]. Chinese Journal of Lasers, 47, 1111002(2020).
[17] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).
[18] Liu L L, Wu F X, Wang Y P et al. Multi-receptive-field CNN for semantic segmentation of medical images[J]. IEEE Journal of Biomedical and Health Informatics, 24, 3215-3225(2020).
[19] Masood A, Sheng B, Yang P et al. Automated decision support system for lung cancer detection and classification via enhanced RFCN with multilayer fusion RPN[J]. IEEE Transactions on Industrial Informatics, 16, 7791-7801(2020).
[20] Abdulwahab S, Rashwan H A, García M Á et al. Adversarial learning for depth and viewpoint estimation from a single image[J]. IEEE Transactions on Circuits and Systems for Video Technology, 30, 2947-2958(2020).
[21] Chen F M, Wen C, Xie K et al. Face liveness detection: fusing colour texture feature and deep feature[J]. IET Biometrics, 8, 369-377(2019).
[22] Chen H, Deng F. Hyperspectral remote sensing image classification based on decomposed three-dimensional convolutional neural network[J]. Science of Surveying and Mapping, 45, 96-102, 129(2020).
[23] Sabokrou M, Fayyaz M, Fathy M et al. Deep-cascade: cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes[J]. IEEE Transactions on Image Processing, 26, 1992-2004(2017).
[24] Zhang H K, Li Y. Spectral-spatial classification of hyperspectral imagery based on deep convolutional network[C]. //2016 International Conference on Orange Technologies (ICOT), December 18-20, 2016, Melbourne, VIC, Australia., 44-47(2016).
[25] Leng J B, Li T, Bai G et al. Cube-CNN-SVM: a novel hyperspectral image classification method[C]. //2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), November 6-8, 2016, San Jose, CA, USA, 1027-1034(2016).
[26] Roy S K, Krishna G, Dubey S R et al. HybridSN: exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 17, 277-281(2020).
[27] Zhang J, Wei F Y, Feng F et al. Spatial-spectral feature refinement for hyperspectral image classification based on attention-dense 3D-2D-CNN[J]. Sensors, 20, 5191(2020).
[28] Han M X, Cong R M, Li X Y et al. Joint spatial-spectral hyperspectral image classification based on convolutional neural network[J]. Pattern Recognition Letters, 130, 38-45(2020).
[29] Zhang B, Zhao L, Zhang X L. Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images[J]. Remote Sensing of Environment, 247, 111938(2020).
[30] Kong F Q, Zhou Y B, Shen Q et al. End-to-end multispectral image compression using convolutional neural network[J]. Chinese Journal of Lasers, 46, 1009001(2019).
[31] Chen Y S, Jiang H L, Li C Y et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 6232-6251(2016).
[32] Hamida A B, Benoit A, Lambert P et al. 3-D deep learning approach for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 4420-4434(2018).
[33] Liu B, Yu X C, Zhang P Q et al. A semi-supervised convolutional neural network for hyperspectral image classification[J]. Remote Sensing Letters, 8, 839-848(2017).