TY - GEN
T1 - Multi-scale structure extraction for hyperspectral image classification
AU - Duan, Puhong
AU - Kang, Xudong
AU - Li, Shutao
AU - Benediktsson, Jon Atli
N1 - Funding Information: This paper is supported by the National Natural Science Fund of China for International Cooperation and Exchanges (No. 61520106001), the National Natural Science Foundation of China (No. 61601179), the National Natural Science Fund of China for Distinguished Young Scholars (No. 61325007), the Fund of Hunan Province for Science and Technology Plan Project under Grant (No. 2017RS3024), and the Science and Technology Plan Projects Fund of Hunan Province (No. 2015WK3001). Publisher Copyright: © 2018 IEEE.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - In this paper, a novel multi-scale structure extraction based spectral-spatial hyperspectral image classification method is proposed, which consists of the following steps. First, the spectral dimension of the hyperspectral image is reduced by averaging adjacent spectral bands. Then, in order to extract the multi-scale significant structural features (MSFs) which are insensitive to image noise and texture, a relative total variation based structure extraction method is applied on the dimension reduced hyperspectral image. Finally, the MSFs are fused together with the kernel principal component analysis (KPCA), so as to obtain the kernel PCA fused multi-scale structural features (KPCA-MSFs) for classification. Experiments conducted on a real hyperspectral image demonstrate the outstanding performance of the proposed approach over several state-of-the-art spectral-spatial classifiers, especially when the image is corrupted by serious scene noise.
AB - In this paper, a novel multi-scale structure extraction based spectral-spatial hyperspectral image classification method is proposed, which consists of the following steps. First, the spectral dimension of the hyperspectral image is reduced by averaging adjacent spectral bands. Then, in order to extract the multi-scale significant structural features (MSFs) which are insensitive to image noise and texture, a relative total variation based structure extraction method is applied on the dimension reduced hyperspectral image. Finally, the MSFs are fused together with the kernel principal component analysis (KPCA), so as to obtain the kernel PCA fused multi-scale structural features (KPCA-MSFs) for classification. Experiments conducted on a real hyperspectral image demonstrate the outstanding performance of the proposed approach over several state-of-the-art spectral-spatial classifiers, especially when the image is corrupted by serious scene noise.
KW - Hyperspectral image classification
KW - Kernel principal component analysis
KW - Structure extraction
UR - https://www.scopus.com/pages/publications/85064250066
U2 - 10.1109/IGARSS.2018.8519425
DO - 10.1109/IGARSS.2018.8519425
M3 - Conference contribution
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 5724
EP - 5727
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -