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.

摘要

在高光谱数据立方体中找到的丰富光谱数据使其在实际应用中有用，例如目标检测。从高光谱数据立方体检测未知背景（例如地面物体）中的目标像素，对于遥感界非常感兴趣。常用的高光谱目标检测方法通常会忽略目标的先验知识问题，并可能降低这些方法的效率。必须注意的是，高光谱数据立方体的空间分辨率通常是有限的。因此，子像素目标仅占据像素的一部分。替换信号模型是亚像素目标的基本模型。在这个研究中，我们基于自动目标生成过程开发了修订的替换信号模型，以使用HyMap数据立方体改善高光谱亚像素目标检测。评估了各种实际目标对高光谱数据立方体的影响，以获得一致的结果。在具有七个目标的实验中，该方法在ROC曲线下的平均面积达到了99％。比较结果表明，与其他最新方法相比，该方法具有竞争目标检测性能。