激光与光电子学进展, 2019, 56 (4): 042801, 网络出版: 2019-07-31
基于低秩稀疏矩阵分解和稀疏字典表达的高光谱异常目标检测 下载: 1193次
Hyperspectral Abnormal Target Detection Based on Low Rank and Sparse Matrix Decomposition-Sparse Representation
遥感 异常检测 高光谱图像 低秩稀疏矩阵分解 稀疏字典表达 remote sensing anomaly detection hyperspectral image low-rank and sparse matrix decomposition sparse representation
摘要
异常目标检测在高光谱图像(HSI)处理领域发挥越来越重要的作用。低秩稀疏矩阵分解算法(LRaSMD)可将背景和异常区分开,可以极大地减弱异常目标对背景的污染。基于此,提出一种基于低秩稀疏矩阵分解和稀疏字典表达(LRaSMD-SR)的高光谱异常目标检测算法,通过LRaSMD的方式获取背景集,通过稀疏表达的方式从背景集中构建背景字典模型,最后通过计算重构误差来检测异常点。该算法在模拟和真实数据上都进行了有效性验证,实验结果证明LRaSMD-SR算法具有非常好的异常目标检测性能。
Abstract
Anomaly detection plays a more and more important role in the hyperspectral image (HIS) processing field. Since the low-rank and sparse matrix decomposition (LRaSMD) algorithm can separate the anomalies from the background, it can protect the background model from corruption by anomalies and noises. A novel hyperspectral anomaly detection algorithm is proposed based on low-rank and sparse matrix decomposition-sparse representation (LRaSMD-SR). First, the relatively pure background is obtained by LRaSMD. Then, the background dictionary model is constructed from the pure background by means of sparse representation. Finally, the reconstruction error is employed to detect the anomalies. The effective experimental tests are conducted using both simulated and real datasets, and the experimental results show that the proposed LRaSMD-SR algorithm possesses a very promising performance of anomaly detection.
张晓慧, 郝润芳, 李廷鱼. 基于低秩稀疏矩阵分解和稀疏字典表达的高光谱异常目标检测[J]. 激光与光电子学进展, 2019, 56(4): 042801. Xiaohui Zhang, Runfang Hao, Tingyu Li. Hyperspectral Abnormal Target Detection Based on Low Rank and Sparse Matrix Decomposition-Sparse Representation[J]. Laser & Optoelectronics Progress, 2019, 56(4): 042801.