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 # ACDA
 Pytorch code of "Hyperspectral Anomaly Change Detection Based on Auto-encoder"
 try "maincode.py"
+
+This is a code of the paper "Hyperspectral anomaly change detection based on autoencoder" implemented on PyTorch.
+Pytorch is needed for running this code.
+
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+[Dataset]: "Viareggio 2013" with de-striping, noise-whitening and spectrally binning
+1.   img_data.mat:    
+img_1(D1F12H1);   img_2(D1F12H2);    img_3(D2F22H2)
+
+链接：https://pan.baidu.com/s/1sRmdjsT-xl6DQJeoPIBNYA 
+提取码：qdqf
+
+
+2.   pretrain_samples:     
+un_idx_train1,un_idx_valid1,un_idx_train2,un_idx_valid2;  [acquired from the pre-detection result of USFA, Wu C, Zhang L, Du B. Hyperspectral anomaly change detection with slow feature analysis[J]. Neurocomputing, 2015, 151: 175-187.]
+3.  groundtruth_samples:     
+un_idx_train1,un_idx_valid1,un_idx_train2,un_idx_valid2;
+4.   random_samples:     un_idx_train1,un_idx_valid1,un_idx_train2,un_idx_valid2;
+
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+[Usage]:  maincode.py
+
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+If you use this code for your research, please cite our papers:
+Hu M, Wu C, Zhang L, et al. Hyperspectral anomaly change detection based on autoencoder[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 3750-3762.