Title :
A Selective Kernel PCA Algorithm for Anomaly Detection in Hyperspectral Imagery
Author :
Gu, Yanfeng ; Liu, Ying ; Zhang, Ye
Author_Institution :
Dept. of Inf. Eng., Harbin Inst. of Technol.
Abstract :
In this paper, a selective kernel principal component analysis algorithm is proposed for anomaly detection in hyperspectral imagery. The proposed algorithm tries to solve the problem brought by high dimensionality of hyperspectral images in anomaly detection. This algorithm firstly performs kernel principal component analysis (KPCA) on the original data to fully mine high-order correlation between spectral bands. Then, high-order statistics in local scene are exploited to define local average singularity (LAS), which is used to measure the singularity of each nonlinear principal component transformed. Based on LAS, one component transformed with maximum singularity is selected after KPCA. Finally, with RX detector, anomaly detection is performed on the component selected. Numerical experiments are conducted on real hyperspectral images collected by AVIRIS. The results prove that the proposed algorithm outperforms the conventional RX algorithm
Keywords :
higher order statistics; object detection; principal component analysis; remote sensing; anomaly detection; high-order correlation; high-order statistics; hyperspectral imagery; local average singularity; principal component analysis; selective kernel PCA algorithm; Detection algorithms; Detectors; Eigenvalues and eigenfunctions; Hyperspectral imaging; Hyperspectral sensors; Kernel; Layout; Object detection; Principal component analysis; Statistics;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1660445