期望最大化聚类的高光谱亚像素目标检测

Abstract

Abstract:

Background is a key interferene in target detection. To avoid the interferene of complex background, an expectation-maximization cluster based approach to subpixel detection in hyperspectral is presented that incorporates background segmentation to model complex background. First, the expectation-maximization cluster model is employed to segment whole background into homogenous regions. Then the adaptive matched subspace detection algorithm (AMSD) is applied in each homogenous region. Since the segmented regions are more homogenous than the whole complex background, our new approach can have a better performance. Experimental result with a real hyperspectral image has proved the validity of the new approach.

Key words: remote sensing, hyperspectral, subpixel target, target detector, image segmentation

CLC Number:

TN219

LIU De-lian;WANG Bo;ZHANG Jian-qi. Hyperspectral subpixel target detection approach based on expectation-maximization cluster[J].J4, 2009, 36(3): 512-516.

References

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Hyperspectral subpixel target detection approach based on expectation-maximization cluster

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