局部联合稀疏指数表示的高光谱图像异常检测

Hyperspectral Image Anomaly Detection Based on Local Joint Sparse Representation Index

针对稀疏表示在高光谱图像异常检测中取得了较好的检测效果,采用局部联合稀疏指数表示的方法,即将局部光谱稀疏指数和局部空间稀疏指数相结合。讨论了窗口设计对检测结果的影响。提出了将自动子空间划分和基于局部联合稀疏指数检测方法相结合的算法,提高了检测的效果。使用合成和真实的高光谱图像数据分别进行了仿真实验,实验结果表明,所提出的方法在检测效果上有一定程度的提高,且不同的窗口设计对检测结果也会产生影响。

Sparse representation had achieved very good results in hyperspectral imaging anomaly detections. A local joint sparse index method was employed, which combined local spectral sparse index and local spatial sparse index. The influence of the window design on the detection results was discussed. The algorithm combining the adaptive subspace decomposition and the detection method based on local joint sparse index was proposed to improve the detection effect. With synthetic and real hyperspectral imaging datasets in the simulation experiment, the results show that the algorithms utilizing the new models could improve the effectiveness of the detection results to a certain degree, and different window designs have an impact on the results.