目的 高光谱异常检测由于其重要的应用价值，引起了研究人员的广泛关注，但大部分的检测算法，往往直接利用输入的高光谱遥感影像所携带的光谱信息或者空谱信息进行检测。考虑到由于成像过程的限制，如成像条件的复杂性以及光谱通道众多导致的每个通道光子数量有限等问题，所获取的高光谱遥感影像往往在一定程度上偏离真实场景，而这也制约了异常检测的精度。针对此问题，本文提出了一种局部梯度轮廓变换的高光谱遥感影像异常检测算法。方法 为了在不影响算法性能的基础上减少计算复杂度，首先选取部分可能的异常像元，只对这些局部的异常像元可能位置进行梯度轮廓变换。其次，将变换后的梯度轮廓用于指导原始高光谱遥感影像的空域增强。最后，对增强后的高光谱遥感影像进行检测。通过将局部梯度轮廓用于影像的增强，避免了成像过程中由于细节损失而造成检测精度受限的情况。结果 实验在来自4个数据集的6幅高光谱遥感影像上进行了性能验证。首先利用经典的Global-RX （Reed Xiaoli）检测算法同时检测本文算法增强后的影像和原始影像，分别取得的平均AUC （area under curve）值为0.987 1和0.933 6，本文算法带来了0.053 5的精度提升；同时，通过与其他3种预处理方法进行比较，证明了本文局部梯度轮廓变换方法的有效性；更进一步，利用基于协同表示CRD （collaborative representation-based detector）的检测器对增强后的影像和原始影像分别进行检测，分别取得的平均AUC值为0.990 7和0.977 5，检测结果再次验证了本文算法能够有效提升影像的检测精度；通过对比，实验数据表明本文所采用的局部梯度轮廓变换可减少约37.82%的时间复杂度。结论 本文算法通过将局部的梯度轮廓进行变换并用于指导原始影像的增强过程，使得影像的空间轮廓信息更为锐利，更为接近真实场景，从而获得异常检测结果的提升。

Objective Anomaly detection is a fundamental problem in hyperspectral remote sensing image processing, and it attracts the interests of several researchers. The anomalies usually refer to the outliers with spectral and spatial signatures that differ from their surroundings. Compared with the background, the anomalies have two main characteristics. First, their spectra are severely different from those of their surroundings, and this phenomenon is called the spectral difference. Meanwhile, the anomalies are usually embedded into the local homogeneous background in a format of several pixels, and this phenomenon is named the spatial difference. Hyperspectral anomaly detection has been widely used in military and civilian applications, such as surveillance, disaster warning, and rescue. In most traditional approaches, anomalies are directly derived from the original hyperspectral image (HSI). However, the HSIs usually deviate from the real scene as limited by the imagery process, such as the complexity of the imagery condition and the limited number of electrons caused by the hundreds of bands. This deviation could reduce the anomaly detection precision. We propose a novel hyperspectral anomaly detection method via the transformation of local gradient profiles to deal with the limitations caused by the low spatial quality. The gradient profile is a 1D profile along the gradient direction of the edge pixel in the image and has been introduced in natural image super resolution. Observations have demonstrated that the shape statistics of the gradient profiles in natural image is quite stable and invariant. In this way, the statistical relationship of the sharpness of the gradient profile between the real scene and the input HSI can be utilized to transform the gradient profiles of the input HSI. Meanwhile, the transformation is applied locally to some probable anomalies to reduce the computational complexity and avoid the disturbance of the background. These transformed gradient profiles are used to provide a constraint on the enhanced HSI. Method A novel hyperspectral anomaly detection method is proposed in this study. Some probable anomalies are coarsely selected via a threshold to reduce the computational complexity without affecting the detection performance. Specifically, the original HSI is detected via the classical Global-RX(Reed Xiaoli) detector, and the responses in the map are sorted and selected. Meanwhile, the gradient profiles of these coarsely selected anomalies are computed and transformed to obtain the sharper versions. Specifically, the distribution of the gradient profile is fitted by a generalized Gaussian distribution. The transformation from the input gradient profile to the desired one can be computed via a transformation formulation. These transformed gradient profiles are closer to those of the real scene than the original gradient profiles. The original HSI is enhanced with these transformed gradient profiles. Experimental data contain six real HSIs coming from four datasets. The original six HSIs and their enhanced versions are detected via the Global-RX detector. Experimental results demonstrate the necessity of the enhancement. Meanwhile, experimental results on detection accuracy superiority of the proposed method over some other preprocessing techniques, such as the discrete wavelet transformation (DWT-RX), the spectral derivatives (Deriv-RX), and the fractional Fourier entropy (FrFE-RX), further validate the effectiveness of our proposed local gradient profile transformation strategy. We utilize the collaborative representation-based detector (CRD) to detect the enhanced and original HSIs. The enhanced HSIs still achieve higher detection accuracy. Result We incorporate six HSIs coming from four datasets, namely, San Diego, AVIRIS(airborne visible/infrared imaging spectrometer)-2, Airport, and Beach, to validate the performance of the proposed method. The quantitative evaluation metrics include the receiver operating curves and the area under the curve (AUC) value. We also exhibit the detection maps of each method for comparison. We validate the necessity of the enhancement. Thus, comparison of detection accuracy is made between the original and enhanced HSIs via the Global-RX detector. AUC values for the six original HSIs are 0.940 2, 0.934 1, 0.840 3, 0.952 5, 0.980 6, and 0.953 8, respectively. The corresponding AUC values for the enhanced HSIs are 0.977 8, 0.984 9, 0.983 5, 0.982 4, 0.998 6, and 0.995 6. Notably, the enhanced HSI always achieves a higher detection accuracy than the original HSI, which proves the necessity of the enhancement. We also compare our proposed method with three other preprocessing techniques, namely, the DWT-RX, Deriv-RX, and the FrFE-RX, which have average AUC values of 0.956 8, 0.957 9, and 0.964 0, respectively. Our proposed method with an average AUC value of 0.987 1 outperforms all the comparison methods. We also utilize the CRD to further validate the effectiveness of our proposed method. The AUC values for the original HSIs detected by the CRD are 0.977 4, 0.985 5, 0.983 6, 0.977 2, 0.991 6, and 0.939 3. The corresponding AUC values for the enhanced HSIs also detected by the CRD are 0.984 0, 0.987 7, 0.990 3, 0.988 8, 0.998 5, and 0.995 0. Notably, enhanced HSIs always outperform the original HSI via the CRD detector. Therefore, the gradient profile transformation is not only effective in promoting the detection accuracy but also outperforms the other preprocessing techniques. Comparing the time required by local and global gradient contour transforms shows that the former can reduce the time complexity by approximately 37.82%. Conclusion In this study, we propose a novel hyperspectral anomaly detection method that incorporates a local gradient profile transformation to enhance the spatial information of the HSIs before detection. The experiment is conducted on six HSIs from four datasets. Experimental results show that our method outperforms several state-of-the-art anomaly detection approaches. The enhanced HSI and original HSI are detected by the Global-RX and the CRD, respectively. The experimental data demonstrate that the enhanced HSI always achieves a superior detection accuracy.