Improving hyperspectral sub-pixel target detection in multiple target signatures using a revised replacement signal model

Abstract

The rich spectral data found in the hyperspectral data cube make them useful in real-world applications, such as target detection. Target pixels detection among an unknown background such as ground objects from hyperspectral data cube is of great interest for remote sensing community. The commonly used hyperspectral target detection methods often overlook the problem of prior knowledge of the target and could reduce the efficiency of these methods. It has to be noted that the spatial resolution of the hyperspectral data cube is usually limited; therefore, the sub-pixel targets only occupy part of the pixel. The replacement signal model is an essential model for sub-pixel targets. In this study, we developed a revised replacement signal model based on an automatic target generation procedure for improving hyperspectral sub-pixel target detection using the HyMap data cube. The effects of various real targets on hyperspectral data cube are evaluated to obtain consistent results. In experiments with seven targets, the proposed method achieves the average area under the ROC curve of 99%. Comparison results illustrated that the proposed method has competitive target detection performance in comparison with other state-of-the-art methods.