|
|
|
|
|
|
Band selection method for hyperspectral image based on linear representation |
| DONG Anguo, GONG Wenjuan, HAN Xue |
| School of Science, Chang’an University, Xi’an 710064, China |
|
|
|
|
Abstract In order to remove the data redundancy of hyperspectral image and improve the accuracy and efficiency of hyperspectral image processing, this paper proposes a band selection method based on linear representation of hyperspectral image. A linear relationship is established for a band with the other bands, and the most relevant band is removed as a redundant band which is determined based on the multiple correlation coefficient. The set of minimum bands is finally obtained by repeating the above process for the remaining bands. It is proved that the set of selected endmembers by using the above bands is consistent with the set selected by using all bands, and the redundancy bands are removed to the greatest extent without affecting the endmember extraction. The experimental results show that the band selection algorithm in the paper is feasible and effective.
|
| Keywords
ZY1-02C
geopark
geo-heritage
remote sensing
3D
|
|
|
|
Issue Date: 04 December 2017
|
|
|
[1] Jia X P,Kuo B C,Crawford M M.Feature mining for hyperspectral image classification[J].Proceedings of the IEEE,2013,101(3):676-697.
[2] Agarwal A,El-Ghazawi T,El-Askary H,et al.Efficient hierarchical-PCA dimension reduction for hyperspectral imagery[C]//Proceedings of 2007 IEEE international symposium on signal processing and information technology.Giza:IEEE,2007:353-356.
[3] Lei W,Prasad S,Fowler J E,et al.Locality-preserving dimensionality reduction and classification for hyperspectral image analysis[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(4):1185-1198.
[4] Jia S,Tang G H,Zhu J S,et al.A novel ranking-based clustering approach for hyperspectral band selection[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(1):88-102.
[5] Feng J,Jiao L C,Zhang X R,et al.Hyperspectral band selection based on trivariate mutual information and clonal selection[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(7):4092-4105.
[6] Hossain M A,Jia X P,Pickering M.Subspace detection using a mutual information measure for hyperspectral image classification[J].IEEE Geoscience Remote Sensing Letters,2014,11(2):424-428.
[7] Sun K,Geng X R,Ji L Y.Exemplar component analysis:A fast band selection method for hyperspectral imagery[J].IEEE Geoscience Remote Sensing Letters,2015,12(5):998-1002.
[8] Patra S,Modi P,Bruzzone L.Hyperspectral band selection based on rough set[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(10):5495-5503.
[9] Medjahed S A,Saadi T A,Benyettou A,et al.Gray Wolf Optimizer for hyperspectral band selection[J].Applied Soft Computing,2016,40:178-186.
[10] Geng X R,Sun K,Ji L Y,et al.A fast volume-gradient-based band selection method for hyperspectral image[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(11):7111-7119.
[11] Su H J,Yong B,Du Q.Hyperspectral band selection using improved firefly algorithm[J].IEEE Geoscience Remote Sensing Letters,2016,13(1):68-72.
[12] Wang C,Gong M G,Zhang M Y,et al.Unsupervised hyperspectral image band selection via column subset selection[J].IEEE Geoscience Remote Sensing Letters,2015,12(7):1411-1415.
[13] Feng J,Jiao L C,Liu F,et al.Mutual-information-based semi-supervised hyperspectral band selection with high discrimination,high information,and low redundancy[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(5):2956-2969.
[14] 苏红军,盛业华,He Y,等.基于正交投影散度的高光谱遥感波段选择算法[J].光谱学与光谱分析,2011,31(5):1309-1313.
Su H J,Sheng Y H,He Y,et al.Orthogonal projection divergence-based hyperspectral band selection[J].Spectroscopy and Spectral Analysis,2011,31(5):1309-1313.
[15] Gao L R,Gao J W,Li J,et al.Multiple algorithm integration based on ant colony optimization for endmember extraction from hyperspectral imagery[J].IEEE Journal of Selected Topics in Applied Earth Observations in Remote Sensing,2015,8(6):2569-2582. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
2017,
Vol. 29
