Traditional hyperspectral target detection methods use spectral domain information for target recognition. Although it can effectively retain intrinsic characteristics of substances, targets in homogeneous regions still cannot be effectively recognized. By projecting the spectral domain features on the transform domain to increase the separability of background and target, fractional domain-based revised constrained energy minimization detector is proposed. Firstly, the fractional Fourier transform is adopted to project the original spectral information into the fractional domain for improving the separability of background and target. Then, a newly revised constrained energy minimization detector is performed, where sliding double window strategy is used to make the best of the local spatial statistical characteristics of testing pixel. In order to make the best of inner window information, the mean value of Pearson correlation coefficient is measured between prior target pixel and testing pixel associated with its four neighborhood pixels. Extensive experiments for four real hyperspectral scenes indicate that the performance of the proposed algorithm is excellent when compared with other related detectors.

传统的高光谱目标检测方法使用光谱域信息进行目标识别。虽然它可以有效地保留物质的内在特征，但仍然无法有效识别同质区域中的目标。通过将谱域特征投影到变换域上以增加背景和目标的可分离性，提出了基于分数域的修正约束能量最小化检测器。首先，采用分数阶傅里叶变换将原始光谱信息投影到分数域中，以提高背景和目标的可分离性。然后，执行新修改的约束能量最小化检测器，其中使用滑动双窗口策略来充分利用测试像素的局部空间统计特性。为了充分利用内窗信息，在先验目标像素与其四个邻域像素相关的测试像素之间测量Pearson相关系数的平均值。对四个真实高光谱场景的大量实验表明，与其他相关检测器相比，该算法的性能非常出色。