Nonlinear Anomaly Detection Based on Spectral–Spatial Composite Kernel for Hyperspectral Images

Anomaly detection is an important task among hyperspectral applications, which detects the anomalous targets that are spectrally different from their surroundings. Most of the anomaly detectors construct the detection models in the linear space, where the nonlinear characteristics of data are not taken into consideration. The kernel-based methods, which implicitly map the data into a high-dimensional feature space, have shown great potential in dealing with nonlinear problems. Nevertheless, the original kernel-based detection methods only exploit the spectral features without taking advantage of the spatial correlations among adjacent pixels. In this letter, a nonlinear spectral-spatial composite kernel-based detector (SSCKD) is proposed for hyperspectral anomaly detection. First, superpixel segmentation is adopted to extract spatial features according to local homogeneity. Then, considering the nonlinear characteristics in the hyperspectral data, spectral and spatial features are combined by the composite kernel. Finally, an iterative composite kernel-learning procedure based on the centered kernel alignment is designed to adaptively determine the kernel weights. The experiments are conducted on three real hyperspectral data sets. The detection results demonstrate the superiority of the proposed method in comparison with the conventional and state-of-the-art anomaly detection methods.