Hybrid Detectors Based on Selective Endmembers

Abstract

Subpixel target detection is a challenge in hyperspectral image analysis. As the spatial resolution of hyperspectral imagery is usually limited, subpixel targets only occupy part of the pixel area. In such cases, the spatial characteristics of the targets are hard to acquire, and the only information we can use comes from spectral characteristics. Several kinds of method based on spectral characteristics have been proposed in the past. One is the linear unmixing method, which can provide the abundances of different endmembers in the hyperspectral imagery, including the target abundance. Another focuses on providing statistically reliable rules to separate subpixel targets from their backgrounds. Recently, hybrid detectors combining the aforementioned two methods were put forward, which cannot only figure out the quantitative information of the endmembers but also put this quantitative information into an adaptive matched subspace detector or adaptive cosine/coherent estimate detector to separate the target pixels from the background with statistically reliable rules. However, in these methods, all the endmembers are used to construct the statistical rule, while in most cases only some of the endmembers are actually contained in the pixels. This paper proposes hybrid endmembers selective detectors in which different kinds of endmembers are used according to different pixels to ensure that the true composition of endmembers in each pixel is applied in the detection procedure. Three different types of hyperspectral data were used in our experiments, and our proposed hybrid endmember selective detectors showed better performances than the current hybrid detectors in all the experiments.