TY - GEN
T1 - Spectral-Spatial Classification of Hyperspectral Image Using PCA and Gabor Filtering
AU - Yan, Qingyu
AU - Zhang, Junping
AU - Feng, Jia
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - The combination of spectral information and spatial context is known to be a suitable way in improving classification accuracy for hyperspectral image. In this paper, a novel method using PCA and spatial filtering for the classification of hyperspectral image is proposed. Firstly, PCA is used to extract spectral information from the hyperspectral image. Secondly, spatial filters containing a set of 2-D Gabor filters and rolling guidance filters (RGF) are convolved with the principal components to extract the subtle spatial texture and edge features respectively. Thirdly, the obtained features are concatenated together as a feature cube to be classified by SVM. The proposed method is thus named as PCA-GR. Experimental results on two real hyperspectral image data sets demonstrate the significant advantages of the proposed method over the compared ones.
AB - The combination of spectral information and spatial context is known to be a suitable way in improving classification accuracy for hyperspectral image. In this paper, a novel method using PCA and spatial filtering for the classification of hyperspectral image is proposed. Firstly, PCA is used to extract spectral information from the hyperspectral image. Secondly, spatial filters containing a set of 2-D Gabor filters and rolling guidance filters (RGF) are convolved with the principal components to extract the subtle spatial texture and edge features respectively. Thirdly, the obtained features are concatenated together as a feature cube to be classified by SVM. The proposed method is thus named as PCA-GR. Experimental results on two real hyperspectral image data sets demonstrate the significant advantages of the proposed method over the compared ones.
KW - Hyperspectral image classification
KW - rolling guidance filter
KW - spatial texture information
UR - https://www.scopus.com/pages/publications/85101972379
U2 - 10.1109/IGARSS39084.2020.9324555
DO - 10.1109/IGARSS39084.2020.9324555
M3 - 会议稿件
AN - SCOPUS:85101972379
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 513
EP - 516
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -