Nonlocal Correntropy Matrix Representation for Hyperspectral Image Classification

Guochao Zhang1; Xueting Hu2; Yantao Wei1; Weijia Cao3; Huang Yao1; Xueyang Zhang1; Keyi Song1

2023

10.1109/LGRS.2023.3248799

IEEE Geoscience and Remote Sensing Letters   影响因子和分区

1558-0571

1558-0571

Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. However, it is challenging to fully use spatial-spectral information for HSI classification due to the high dimensionality of the data, high intraclass variability, and the limited availability of training samples. To deal with these issues, we propose a novel feature extraction method called nonlocal correntropy matrix (NLCM) representation in this letter. NLCM can characterize the spectral correlation and effectively extract discriminative features for HSI classification. We verify the effectiveness of the proposed method on two widely used datasets. The results show that NLCM performs better than the state-of-the-art methods, especially when the training set size is small. Furthermore, the experimental results also demonstrate that the proposed method outperforms compared methods significantly when the land covers are complex and with irregular distributions.

Feature extraction hyperspectral image (HSI) classification nonlocal correntropy matrix (NLCM)

SCI ; EI

英语

其他

National Natural Science Foundation of China[62277029] ; National Collaborative Innovation Experimental Base Construction Project for Teacher Development of Central China Normal University[CCNUTEIII-2021-19] ; Humanities and Social Sciences of China Ministry of Education (MOE)[20YJC880100] ; Knowledge Innovation Program of Wuhan-Basic Research[2022010801010274] ; Fundamental Research Funds for the Central Universities[CCNU22JC011]

Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology

Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:000946308200008

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20231013687939

Classification (of information) ; Extraction ; Hyperspectral imaging ; Image classification ; Image representation ; Image segmentation ; Matrix algebra ; Remote sensing

Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Imaging Techniques:746 ; Chemical Operations:802.3 ; Information Sources and Analysis:903.1 ; Algebra:921.1

IEEE

1.Hubei Research Center for Educational Informationization, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China
2.Department of Electronic and Electrical Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen, China
3.Zhongke Langfang Institute of Spatial Information Applications, Langfang, China

Guochao Zhang,Xueting Hu,Yantao Wei,et al. Nonlocal Correntropy Matrix Representation for Hyperspectral Image Classification[J]. IEEE Geoscience and Remote Sensing Letters,2023,20:1-5.

Guochao Zhang.,Xueting Hu.,Yantao Wei.,Weijia Cao.,Huang Yao.,...&Keyi Song.(2023).Nonlocal Correntropy Matrix Representation for Hyperspectral Image Classification.IEEE Geoscience and Remote Sensing Letters,20,1-5.

Guochao Zhang,et al."Nonlocal Correntropy Matrix Representation for Hyperspectral Image Classification".IEEE Geoscience and Remote Sensing Letters 20(2023):1-5.