A novel hyperspectral image anomaly detection method based on low rank representation
A novel hyperspectral image anomaly detection method based on low rank representation
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DOI：
10.1109/igarss.2015.7326813
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Yang Xu;Zenbin Wu;Zhihui Wei;Hongyi Liu;Xiong Xu
中科院分区：
文献类型：
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作者：
Yang Xu;Zenbin Wu;Zhihui Wei;Hongyi Liu;Xiong Xu
提出了一种基于低秩表示的高光谱图像异常检测方法。在观测到的恒指中，异常可以从背景中分离出来。由于背景中的每一个像素都可以用一个背景字典近似表示，并且背景像素的表示系数是相关的，因此采用低秩表示模型对背景部分进行建模。此外，为了获得鲁棒的表示系数，增加了和一约束。该方法的优点是利用了背景的全局相关性，增强了图像表示的鲁棒性。实验结果已经进行了使用模拟和真实的数据集。这些实验表明，我们的算法取得了非常有前途的性能。
This paper presents a novel method for anomaly detection in hyperspectral image(HSI) based on low-rank representation. In the observed HSI, the anomalies can be separated from the background. Since each pixel in the background can be approximately represented by a background dictionary, and the representation coefficients of the background pixels are correlative, a low-rank representation model is used to model the background part. Besides, to gain robust representation coefficient, the sum-to-one constraint is added. The advantage of the proposed low-rank representation sum-to-one (LRRSTO) method is that it makes use of the global correlation of the background and strength the robustness of the representation. Experiments results have been conducted using both simulated and real data sets. These experiments indicated that our algorithm achieves very promising performance.