联合表示求解二元假设模型的高光谱目标检测

Collaborative Representation-Based Binary Hypothesis Model for Hyperspectral Target Detection

针对稀疏表示目标检测理论中稀疏度难以确定的问题,本文将联合表示应用于目标检测,提出了一种新颖的目标检测算法,并给出了该算法的非线性形式.其核心思想是:背景像元的光谱能够被其周围背景像元的光谱(背景字典)线性表示,而目标像元的光谱只能被其周围背景像元的光谱和目标先验光谱(联合字典)线性表示.该算法首先用背景字典和联合字典分别对待检测像元进行联合表示,然后比较两次联合表示的重构误差确定像元类别.通过真实的高光谱图像进行验证,结果表明,与其它目标检测算法相比,该算法具有较好的检测性能.

In order to solve the problem of setting sparsity level in sparse representation-based target detection algo- rithrns,this paper proposes a novel collaborative representation-based algorithm for hyperspectral target detection, and then extends it into a kernel version. The key idea is that a background pixel can be approximately represented as a linear combi- nation of its surrounding neighbors （background dictionary）, while a target pixel can only be approximately represented as a linear combination of its surrounding neighbors and the prior target spectrums （ union dictionary.）. First the unknown pixel is collaboratively represented by the background dictionary and union dictionary, respectively. Then targets can be determined by comparing the reconstruction residuals. Experimental results on real hyperspectral data set demonstrate the effectiveness of our proposed detector as well as its kernel version when compared ：with other algorithms.