Binary-Class Collaborative Representation for Target Detection in Hyperspectral Images

Hyperspectral target detection refers to an approach that tries to locate targets in a hyperspectral image (HSI) on the condition of given targets spectrum, which plays an important role in hyperspectral remote sensing image processing. In this letter, we propose a binary-class collaborative representation-based detector. The proposed algorithm uses the concept that each background pixel can be approximately represented by its adjacent pixels within a sliding dual-window, and each target pixel can also be approximately represented by some pixels of the image; we use the given target pixels to represent it. Before estimating each background pixel, a background dictionary purification process is proposed to further improve the detector performance. The proposed algorithm was tested on three benchmark HSI data sets, and the experimental results show that the proposed algorithm demonstrates outstanding detection performances when compared with other state-of-the-art detectors.