Weighted Hyperspectral Image Target Detection Algorithm Based on ICA Orthogonal Subspace Projection

Hyperspectral data contained over hundreds of narrow contiguous wavelength bands are extremely suitable for target detection due to their high spectral resolution. In the target detection for hyperspectral image, the background data are not well represented from the original data sources. We propose a weighted hyperspectral image target detection algorithm based on independent component analysis orthogonal subspace projection(ICA\|OSP). The methods start from a collection of independent component of the image pixels, through a spectral similarity measure weighted so that each pixel to give the appropriate weights. It can effectively solve the problems that can not correctly extract the background data from the original image. The problem usually causes a higher false alarm probability. AVIRIS hyperspectral image simulation and detection algorithms are compared by ROC curves with the relevant target detection algorithm, and the results show that the proposed algorithm can reduce the false alarm probability, to better target detection effects.