利用ICA正交子空间投影加权的高光谱影像目标探测算法

提出了一种利用独立成分分析（ICA）正交子空间投影加权的高光谱影像目标探测方法。该方法从影像像元集合的独立成分入手，通过一种光谱相似性测度加权，赋予每个像素合适的权值，从而有效地解决从原始影像中无法正确提取背景数据而造成的虚警概率高的问题。实验结果表明，相比于经典的CEM方法，在相同的探测概率下，该方法能降低1.97%的虚警概率；与相关目标探测算法相比，所提出的算法具有较好的目标探测效果。

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.