Randomized subspace-based robust principal component analysis for hyperspectral anomaly detection
Randomized subspace-based robust principal component analysis for hyperspectral anomaly detection
复制标题
用于高光谱异常检测的基于随机子空间的稳健主成分分析
DOI：
10.1117/1.jrs.12.015015
复制
发表时间：
2018-03
影响因子：
1.7
通讯作者：
Zhang Dianfa
中科院分区：
文献类型：
--
作者：
Sun Weiwei;Yang Gang;Li Jialin;Zhang Dianfa
抽象的。提出了一种基于随机子空间的鲁棒主元分析（RSRPCA）方法用于高光谱图像异常检测。RSRPCA结合了随机列子空间和鲁棒主成分分析（RPCA）的优点。它假设背景具有低秩性质，并且异常是稀疏的，并且不位于背景的列子空间中。首先，RSRPCA实现随机采样，从列中勾勒出原始HSI数据集，并构造背景的随机化列子空间。结构化的随机投影也被用来从行绘制HSI数据集。从列和行绘制可以大大减少RSRPCA的计算需求。其次，RSRPCA采用列RPCA（CWRPCA），以消除采样异常像素的负面影响，并通过删除采样异常列来净化先前的随机化列子空间。CWRPCA将HSI数据的子矩阵分解为低秩矩阵（即，背景分量），噪声矩阵（即，噪声分量），以及稀疏异常矩阵（即，异常分量），其中只有一小部分非零列。利用非精确增广拉格朗日乘子算法对CWRPCA问题进行优化，并估计稀疏矩阵。稀疏异常矩阵的非零列指向子矩阵中的采样异常列。第三，将所有像素投影到背景的纯化随机列子空间的互补子空间上，最终精确定位原始HSI数据中的异常像素。通过对3幅真实的高光谱图像的实验，研究了RSRPCA的检测性能，并与4种现有方法进行了比较。实验结果表明，所提出的RSRPCA在检测性能和计算时间上都优于四种比较方法。
Abstract. A randomized subspace-based robust principal component analysis (RSRPCA) method for anomaly detection in hyperspectral imagery (HSI) is proposed. The RSRPCA combines advantages of randomized column subspace and robust principal component analysis (RPCA). It assumes that the background has low-rank properties, and the anomalies are sparse and do not lie in the column subspace of the background. First, RSRPCA implements random sampling to sketch the original HSI dataset from columns and to construct a randomized column subspace of the background. Structured random projections are also adopted to sketch the HSI dataset from rows. Sketching from columns and rows could greatly reduce the computational requirements of RSRPCA. Second, the RSRPCA adopts the columnwise RPCA (CWRPCA) to eliminate negative effects of sampled anomaly pixels and that purifies the previous randomized column subspace by removing sampled anomaly columns. The CWRPCA decomposes the submatrix of the HSI data into a low-rank matrix (i.e., background component), a noisy matrix (i.e., noise component), and a sparse anomaly matrix (i.e., anomaly component) with only a small proportion of nonzero columns. The algorithm of inexact augmented Lagrange multiplier is utilized to optimize the CWRPCA problem and estimate the sparse matrix. Nonzero columns of the sparse anomaly matrix point to sampled anomaly columns in the submatrix. Third, all the pixels are projected onto the complemental subspace of the purified randomized column subspace of the background and the anomaly pixels in the original HSI data are finally exactly located. Several experiments on three real hyperspectral images are carefully designed to investigate the detection performance of RSRPCA, and the results are compared with four state-of-the-art methods. Experimental results show that the proposed RSRPCA outperforms four comparison methods both in detection performance and in computational time.
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DOI：
10.1109/tgrs.2006.873019
发表时间：
2006-08-01
影响因子：
8.2
作者：
Banerjee, Amit;Burlina, Philippe;Diehl, Chris
通讯作者：
Diehl, Chris
影响因子：
4.8
作者：
Khazai, Safa;Homayouni, Saeid;Mojaradi, Barat
通讯作者：
Mojaradi, Barat
影响因子：
14.9
作者：
N. Nasrabadi
通讯作者：
N. Nasrabadi
影响因子：
4.8
作者：
S. Matteoli;Tiziana Veracini;M. Diani;G. Corsini
通讯作者：
S. Matteoli;Tiziana Veracini;M. Diani;G. Corsini
DOI：
10.1117/12.817843
发表时间：
2009-05
期刊：
--
影响因子：
--
作者：
M. Gartley;William F Basener
通讯作者：
M. Gartley;William F Basener