摘要
低秩稀疏表示广泛应用于高光谱异常检测,为了充分利用字典原子的空谱信息,本文提出了基于空谱背景字典的低秩稀疏表示高光谱异常检测方法。为了使空谱背景字典中包含所有的背景类别,本文利用K-means进行聚类,并对每个类别的像元在局部窗口内计算与邻域像元的特征相似度,从而获取每个类别的残差异常矩阵,进而计算像元的异常度。选择每个类中的代表性原子构成空谱背景字典,然后利用低秩稀疏表示分离出异常部分和背景部分,即用空谱背景字典对原始数据进行重构。在5组高光谱数据集上的实验结果表明,该方法具有较好的检测性能,能够有效地提升检测精度。
Low-rank and sparse representations are widely used for hyperspectral anomaly detection.To fully exploit the spatial-spectral information of dictionary atoms,this study proposes a low-rank and sparse representation hyperspectral anomaly detection algorithm based on a spatial-spectral dictionary.To include all background categories in the spatial-spectral background dictionary,K-means clustering was applied.The feature similarity between pixels of each category and their neighboring pixels within a local window was calculated to obtain the residual difference constant matrix for each category,which was then used to compute the anomaly degree of each pixel.Representative atoms from each class were selected to form the spatialspectral background dictionary,after which the abnormal and background components were separated using low-rank and sparse representations.The original data were reconstructed using this spatial-spectral background dictionary.Experimental results on five hyperspectral datasets demonstrate that the proposed method has good detection performance and can effectively improve the detection accuracy.
作者
田苗
黄远程
李茗欣
刘数数
TIAN Miao;HUANG Yuancheng;LI Mingxin;LIU Shuoshuo(College of Geomatics,Xi'an University of Science and Technology,Xi’an 710054,China;Shaanxi Yanchang Petroleum Balasu Coal Industry Co.,Ltd.,Yulin 719000,China)
出处
《红外技术》
北大核心
2025年第8期990-997,共8页
Infrared Technology
基金
国家自然科学基金(42171394)
痕迹科学与技术公安部重点实验室开放基金(2020FMKFKT07)。
关键词
高光谱图像
异常检测
空谱信息
低秩稀疏
hyperspectral image
anomaly detection
spatial-spectral information
low-rank and sparse
