Band selection of hyperspectral image based on optimal linear prediction of principal components in subspace

WU Yi-Quan; ZHOU Yang; SHENG Dong-Hui; YE Xiao-Lai

doi:10.11972/j.issn.1001-9014.2018.01.021

[1] Wang S, Chang C I. Variable-number variable-band selection for feature characterization in hyperspectral signatures ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2007,45(9):2979-2992.

[2] Jia S, Ji Z, Qian Y. Unsupervised band selection for hyperspectral imagery classification without manual band removal ［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(2): 531-543.

[4] Pal M, Foody G M. Feature selection for classification of hyperspectral data by SVM ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(5):2297-2307.

[6] Yang H, Du Q, Chen G. Unsupervised hyperspectral band selection using graphics processing units ［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011, 4(3):660-668.

[7] Chang C I, Du Q, Sun T L, et al. A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification ［J］. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(6):2631-2641.

[8] Sun K, Geng X, Ji L. An efficient unsupervised band selection method based on an autocorrelation matrix for a hyperspectral image ［J］. International Journal of Remote Sensing, 2014, 35(21):7458-7476.

[10] Dai W, Milenkovic O. Subspace pursuit for compressive sensing signal reconstruction［J］. IEEE Transactions on Information Theory, 2009, 55(5):2230-2249.

[11] Von L U. A tutorial on spectral clustering［J］. Statistics and Computing, 2007, 17(4):395-416.