Potential analysis and prospect of hyperspectral ground object recognition

doi:10.11947/j.AGCS.2023.20220495

Abstract

Abstract: Hyperspectral remote sensing technology has fully released its potential in geospatial information acquisition due to the unique technical advantages in massive data analysis, information fusion, and recognition of ground objects. Due to the nonlinearity of hyperspectral data and complexity of ground objects, there are problems of data heterogeneity and sample scarcity when using hyperspectral images for land cover classification. Classical hyperspectral image analysis methods have the characteristics of a small amount of calculation, fewer labeled samples requirement, and strong theoretical interpretability, which play an important role in the extraction of ground objects attribute information. To cope with the rapid increase in the amount of data and more diverse applications, it is urgent to develop automatic and intelligent hyperspectral recognition technology for land cover classification. In recent years, artificial intelligence methods have been studied and widely applied in the field of hyperspectral remote sensing. In particular, image analysis and information extraction technology based on deep learning has become a persistent hot spot, which has effectively promoted the refinement and intelligence of hyperspectral recognition of ground objects. Based on a brief analysis of the potential and demand of recognition of ground objects, this article systematically summarizes several advancements in hyperspectral image analysis, and focuses on new ideas of deep learning in recent years for the intelligent recognition of hyperspectral ground objects. Firstly, combining the terrain elements and the capability of hyperspectral detection, the hyperspectral ground objects are divided into four categories (i.e., vegetation, soil, water, and artificial buildings) and several sub-categories, and spectral response characteristics of four ground objects are also analyzed; thereafter, in terms of image analysis research, the image analysis technologies especially band selection, feature extraction, pattern classification, and post-classification processing are reviewed, and new research directions and hotspots are also analyzed. In terms of intelligent processing, according to the schedule of supervised learning, semi-supervised learning, and self-supervised learning, the deep neural network models applied in hyperspectral ground object recognition are systematically summarized, and the applications of transfer learning as well as meta learning are also analyzed. Finally, based on the above analysis, the development trends of hyperspectral ground object recognition are also prospected, so as to expand the research ideas in the future.

Key words: hyperspectral ground object recognition, hyperspectral image analysis, deep learning, semi-supervised learning, self-supervised learning, meta learning, transfer learning

CLC Number:

P237

YU Xuchu, LIU Bing, XUE Zhixiang. Potential analysis and prospect of hyperspectral ground object recognition[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(7): 1115-1125.

References

[1] 余旭初,冯伍法,杨国鹏,等. 高光谱影像分析与应用[M]. 北京:科学出版社,2013. YU Xuchu, FENG Wufa, YANG Guopeng, et al. Hyperspectral image analysis and application[M]. Beijing:Science Press, 2013.
[2] 张良培,杜博,张乐飞. 高光谱遥感图像处理[M]. 北京:科学出版社,2012. ZHANG Liangpei, DU Bo, ZHANG Lefei. Hyperspectral remote sensing image processing[M]. Beijing:Science Press, 2012.
[3] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会. GB/T 33462-2016基础地理信息1:10 000地形要素数据规范[S]. 北京:中国标准出版社,2016. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China. GB/T 33462-2016 Fundamental geographic information specifications for 1:10 000 topographic data[S]. Beijing:Standards Press of China, 2016.
[4] 丁胜,袁修孝,陈黎.粒子群优化算法用于高光谱遥感影像分类的自动波段选择[J]. 测绘学报, 2010, 39(3):257-263. DING Sheng, YUAN Xiuxiao, CHEN Li. Automatic band selection of hyperspectral remote sensing image classification using particle swarm optimization[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(3):257-263.
[5] PATRO R N, SUBUDHI S, BISWAL P K, et al. A review on unsupervised band selection techniques:land cover classification for hyperspectral earth observation data[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(3):72-111.
[6] FENG Jie, LI Di, GU Jing, et al. Deep reinforcement learning for semisupervised hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-19.
[7] LIU Bing, YU Anzhu, ZHANG Pengqiang, et al. Active deep densely connected convolutional network for hyperspectral image classification[J]. International Journal of Remote Sensing, 2021, 42(15):5915-5934.
[8] 石茜, 杜博, 张良培. 一种基于局部判别正切空间排列的高光谱遥感影像降维方法[J]. 测绘学报, 2012, 41(3):417-420. SHI Qian, DU Bo, ZHANG Liangpei. A dimensionality reduction method for hyperspectral imagery based on local discriminative tangent space alignment[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(3):417-420.
[9] MURA M D, 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, 2010, 48(10):3747-3762.
[10] LIU Bing, GUO Wenyue, CHEN Xin, et al. Morphological attribute profile cube and deep random forest for small sample classification of hyperspectral image[J]. IEEE Access, 2020, 56:117096-117108.
[11] LI Wei, CHEN Chen, SU Hongjun, et al. Local binary patterns and extreme learning machine for hyperspectral imagery classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(7):3681-3693.
[12] JIA Sen, LIN Zhijie, DENG Bin, et al. Cascade superpixel regularized gabor feature fusion for hyperspectral image classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(5):1638-1652.
[13] LIU Bing, YU Anzhu, TAN Xiong, et al. Slow feature extraction for hyperspectral image classification[J]. Remote Sensing Letters, 2021, 12(5):429-438.
[14] ROMASZEWSKI M, GLOMB P, CHOLEWA M. Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 121:60-76.
[15] LIU Bing, YU Xuchu, YU Anzhu, et al. Spectral-spatial classification of hyperspectral imagery based on recurrent neural networks[J]. Remote Sensing Letters, 2018, 9(12):1118-1127.
[16] 刘冰, 余旭初, 张鹏强, 等. 联合空-谱信息的高光谱影像深度三维卷积网络分类[J]. 测绘学报, 2019, 48(1):53-63. DOI:10.11947/j.AGCS.2019.20170578. LIU Bing, YU Xuchu, ZHANG Pengqiang, et al. Deep 3D convolutional network combined with spatial-spectral features for hyperspectral image classification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1):53-63. DOI:10.11947/j.AGCS.2019.20170578.
[17] WANG Kexian, ZHENG Shunyi, LI Rui, et al. A deep double-channel dense network for hyperspectral image classification[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(4):46-62.
[18] XU Yonghao, ZHANG Liangpei, DU Bo, et al. Spectral-spatial unified networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10):5893-5909.
[19] XUE Zhixiang, YU Xuchu, TAN Xiong, et al. Multiscale deep learning network with self-calibrated convolution for hyperspectral and LiDAR data collaborative classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-16.
[20] XUE Zhixiang, TAN Xiong, YU Xuchu, et al. Deep hierarchical vision transformer for hyperspectral and LiDAR data classification[J]. IEEE Transactions on Image Processing, 2022, 31:3095-3110.
[21] ZHENG Zhuo, ZHONG Yanfei, MA Ailong, at al. FPGA:fast patch-free global learning framework for fully end-to-end hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(8), 5612-5626.
[22] ZHU Lin, CHEN Yushi, PEDRAM G, at al. Generative adversarial networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9):5046-5063.
[23] QIN Anyong, SHANG Zhaowei, TIAN Jinyu, et al. Spectral-spatial graph convolutional networks for semisupervised hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 16(2):241-245.
[24] 左溪冰, 刘冰, 余旭初, 等. 高光谱影像小样本分类的图卷积网络方法[J]. 测绘学报, 2021, 50(10):1358-1369. DOI:10.11947/j.AGCS.2021.20200155. ZUO Xibing, LIU Bing, YU Xuchu, et al. Graph convolutional network method for small sample classification of hyperspectral images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10):1358-1369. DOI:10.11947/j.AGCS.2021.20200155.
[25] LIU Bing, YU Anzhu, YU Xuchu, et al. Deep multiview learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(9):7758-7772.
[26] 陶超, 阴紫薇, 朱庆, 等. 遥感影像智能解译:从监督学习到自监督学习[J]. 测绘学报, 2021, 50(8):1122-1134. DOI:10.11947/j.AGCS.2021.20210089. TAO Chao, YIN Ziwei, ZHU Qing, et al. Remote sensing image intelligent interpretation:from supervised learning to self-supervised learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1122-1134. DOI:10.11947/j.AGCS.2021.20210089.
[27] XUE Zhixiang, YU Xuchu, YU Anzhu, et al. Self-supervised feature learning for multimodal remote sensing image land cover classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-15.
[28] HE Xin, CHEN Yushi, PEDRAM G. Heterogeneous transfer learning for hyperspectral image classification based on convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(5):3246-3263.
[29] WANG Haoyu, CHEN Yushi, CHEN C L P, et al. Hyperspectral image classification based on domain adversarial broad adaptation network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-13.
[30] GAO Kuiliang, GUO Wenyue, YU Xuchu, et al. Deep induction network for small samples classification of hyperspectral images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:3462-3477.