Hyperspectral Abnormal Target Detection Based on Low Rank and Sparse Matrix Decomposition-Sparse Representation

Fig. 2. Real data. (a) Pseudo color image on AVIRIS airplane data; (b) ground truth on AVIRIS airplane data; (c) pseudo color image on HYDICE urban data; (d) ground truth on HYDICE urban data

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Fig. 3. Visualization of detection results on simulated datasets. (a) GRX; (b) LRX; (c) LRaSMD; (d) SRD; (e) LRaSMD-SR

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Fig. 4. ROC curves. (a) Simulated data; (b) AVIRIS airplane true data; (c) HYDICE urban data

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Fig. 5. Visualization of detection results on AVIRIS dataset. (a) GRX; (b) LRX; (c) LRaSMD; (d) SRD; (e) LRaSMD-SR

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Fig. 6. Visualization of detection results on HYDICE dataset. (a) GRX; (b) LRX; (c) LRaSMD; (d) SRD; (e) LRaSMD-SR

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Table 1. AUC theoretical value by detection algorithms
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Table 1. AUC theoretical value by detection algorithms
Algorithm Simulateddata AVIRISairplane HYDICEurban
GRX 1 0.9357 0.8500
LRX 0.9995 0.9631 0.9071
LRaSMD 0.9996 0.9874 0.7277
SRD 0.9141 0.9389 0.9175
LRaSMD-SR 1 0.9908 0.9705

Table 2. AUC experimental results by detection algorithms
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Table 2. AUC experimental results by detection algorithms
GRX LRX LRaSMD SRD LRaSMD-SR
0.85 0.9071 0.7184 0.8737 0.9678
0.85 0.9071 0.7132 0.9176 0.9599
0.85 0.9071 0.7159 0.9002 0.9705
0.85 0.9071 0.7277 0.8742 0.9679
0.85 0.9071 0.7009 0.885 0.9651

Table 3. Significance analysis
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Table 3. Significance analysis
Source ofvariance Square sum ofdispersion Degree offreedom Average sum ofsquares F value Significance
Inter-group 0.18 4 0.045 450 *
Intra-group 0.0018 20 0.0001

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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

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