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Low-Rank and Sparse Decomposition with Mixture-of-Gaussian for Hyperspectral Anomaly Detection
你好,首先非常感谢您能够分享有关高光谱异常检测的代码,这对我的学习非常有用;有几个问题想请教一下您;
在RPCA-RX算法中,您的代码好像直接计算的是稀疏(异常)部分的马氏距离,而原文好像计算的是欧氏距离,所以我有点疑惑
tic;
[r0 Output_S Output_L] = Unsupervised_RPCA_Detect_v1(DataTest);
toc;
XS = reshape(Output_S, M, N);
r2 = RX(XS');
在LSMAD算法中,计算背景协方差矩阵的逆时,作者用的是最大的几个特征值和其对应的特征向量,所以您这块的代码我有点不明白
tic;
[L,S,RMSE,error]=GoDec(Y',28,floor(0.0022*N)*9,2);
toc
L=L';
S=S';
mu=mean(L,2);
r_new2=(diag((Y-repmat(mu,[1,M]))'pinv(cov(L'))(Y-repmat(mu,[1,M]))))';
最后,非常非常感谢您!
the title: ''Low-Rank and Sparse Decomposition with Mixture-of-Gaussian for Hyperspectral Anomaly Detection'' is your new paper? I can't find it, can you put it in your github
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