基于光谱空间重构的非监督最邻近规则子空间的高光谱异常检测

针对高光谱遥感影像维数高、数据量巨大且地物分布复杂,导致背景与异常难以区分的问题,提出一种基于光谱空间重构的非监督最邻近规则子空间异常探测算法.首先通过基于结构张量的波段选择算法,去除噪声像元,选择更有效的波段.然后,通过光谱空间重构增加背景与异常的绝对光谱距离.最后,为了充分利用背景字典之间的空间相似性信息,将空间距离权重引入到非监督最邻近规则子空间算法中,提高检测精度.为验证所提算法的有效性,用四组真实的高光谱数据进行实验,研究了不同参数对检测结果的影响.结果表明,与其他异常检测算法对比,所提算法具有更好的检测效果.

Unsupervised Nearest Regularized Subspace Based on Spectral Space Reconstruction for Hyperspectral Anomaly Detection

The high dimension and huge data volume of hyperspectral remote sensing images and the complexity of surface feature lead to difficulty in distinguishing the anomaly pixel from the background.To solve these problems,an unsupervised nearest regularized subspace anomaly detection algorithm based on spectral space reconstruction is proposed.Firstly,in the process of band selection based on structure tensor,noise pixels are removed to obtain more effective bands.Then,the spectral space reconstruction is utilized to increase the absolute spectral distance between the background and the anomaly.Finally,to take full advantage of the spatial similarity information between background dictionaries,the spatial distance weight is introduced into the unsupervised nearest regularized subspace algorithm to improve the accuracy of linear representation.To validate the effectiveness of the proposed algorithm,experiments on four sets of real hyperspectral data are conducted,and the infulence of different parameters on the detection results is studied.Experimental results demonstrate that the proposed algorithm has a better detective performance than other anomaly detection algorithms.