Anomaly Detection Based on Selective Segmentation Row-Column Two-Dimensional Principal Component Analysis for Hyperspectral Images

Yang Guang; Xiang Yingjie; Wang Qi; Tian Zhangnan

doi:10.3788/lop55.081002

[1] Zhang B. Advances of hyperspectral image processing and information extraction［J］. Journal of Remote Sensing, 2016, 20(5): 1062-1090.

[2] Tong Q,Xue Y, Zhang L. Progress in hyperspectral remote sensing science and technology in China over the past three decades［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 70-91.

[3] Pu H Y, Wang B, Zhang L M. New dimensionality reduction algorithms for hyperspectral imagery based on manifold learning［J］. Infrared and Laser Engineering, 2014, 43(1): 232-237.

[4] Yang J, Zhang D, Frangi A F, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137.

[5] Zhou Y, Li X R, Zhao L Y. Modified linear-prediction based band selection for hyperspectral image［J］. Acta Optica Sinica, 2013, 33(8): 0828002.

[6] Guo L, Chang W W, Fu Z Y. Band selection of optimal for hyperspectral image fusion［J］. Journal of Astronautics, 2011, 32(2): 374-379.

[7] Zhao C H, Hu C M, Shi H. Anomaly detection for a hyperspectral image by using a selective section principal component analysis algorithm［J］. Journal of Harbin Engineering University, 2011, 32(1): 109-113.

[8] Kang X D, Li S T, Jon A B. Feature extraction of hyperspectral images with image fusion and recursive filtering［J］. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(6): 3742-3752.

[9] Stefanou M S, Kerekes J P. A method for assessing spectral image utility［J］. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(6): 1698-1706.

[10] Yuan Y, Wang Q, Zhu G K. Fasthyperspectral anomaly detection via high-order 2-D crossing filter［J］. IEEE Transactions on Geoscience and Remote Sensing, 2014, 53(2): 620-630.