Spectroscopy and Spectral Analysis, Volume. 42, Issue 4, 1270(2022)
Hyperspectral Anomaly Detection Based on 3D Convolutional Autoencoder Network
Figures & Tables(12)
Fig. 1. Comparing 2D convolution operation (a) and 3D convolution operation (b)
Fig. 4. Results of San Diego datasets anomaly detection
(a): False color image; (b): Reference map; (c): RX; (d): SRX; (e): CRD; (f): UNRS; (g): LRASR; (h): 3D-CAEAD
Fig. 5. Results of Los Angeles datasets anomaly detection
(a): False color image; (b): Reference map; (c): RX; (d): SRX; (e): CRD; (f): UNRS; (g): LRASR; (h): 3D-CAEAD
Fig. 6. Results of Pavia datasets anomaly detection
(a): False color image; (b): Reference map; (c): RX; (d): SRX; (e): CRD; (f): UNRS; (g): LRASR; (h): 3D-CAEAD
Table 1. Information of datasets
View tableView in ArticleTable 1. Information of datasets
名称 传感器 波长/μm 像素 波段 San Diego-1 AVRIS 0.37~2.51 100×100 205 San Diego-2 AVRIS 0.37~2.51 100×100 191 San Diego-3 AVRIS 0.37~2.51 120×120 189 San Diego-4 AVRIS 0.37~2.51 100×100 189 Los Angeles-1 AVRIS 0.45~1.35 100×100 204 Los Angeles-2 AVRIS 0.45~1.35 100×100 207 Pavia-1 ROSIS 0.43~0.86 100×100 188 Pavia-2 ROSIS 0.43~0.86 100×100 188 Pavia-3 ROSIS 0.43~0.86 100×100 102
Table 2. 3D-CAE parameter settings
View tableView in ArticleTable 2. 3D-CAE parameter settings
卷积层 INPUT
SIZEKERNEL
SIZEKERNEL
NUMBERSSTRIDE PADDING OUTPUT
SIZECONV1 224 5 5 1 2×3×3 32 2 1 1 valid 112 3 3 32 CONV2 112 3 3 32 2×1×1 64 2 1 1 valid 56 3 3 64 CONV3 56 3 3 64 2×3×3 128 2 1 1 valid 28 1 1 128 DECONV1 28 1 1 128 2×3×3 64 2 1 1 valid 56 3 3 64 DECONV2 56 3 3 64 2×1×1 32 2 1 1 valid 112 3 3 32 DECONV3 112 3 3 32 2×3×3 1 2 1 1 valid 224 5 5 1
Table 3. AUC values of three groups of datasets
View tableView in ArticleTable 3. AUC values of three groups of datasets
数据集 RX SRX CRD UNRS LRASR 3D-CAEAD San Diego-1 0.840 4 0.902 5 0.884 0 0.898 7 0.866 0 0.971 7 San Diego-2 0.952 6 0.944 1 0.768 1 0.791 7 0.962 2 0.986 9 San Diego-3 0.911 1 0.949 5 0.760 2 0.812 6 0.988 7 0.978 4 San Diego-4 0.940 3 0.961 7 0.907 1 0.919 8 0.894 9 0.983 3 Los Angeles-1 0.990 7 0.724 1 0.965 5 0.972 2 0.954 0 0.993 0 Los Angeles-2 0.944 0 0.927 5 0.922 4 0.981 6 0.989 9 0.984 6 Pavia-1 0.980 7 0.966 4 0.992 8 0.992 9 0.918 7 0.987 8 Pavia-2 0.999 1 0.995 8 0.994 4 0.997 6 0.996 3 0.999 9 Pavia-3 0.953 8 0.877 7 0.886 8 0.959 9 0.930 5 0.972 4
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Sheng-ming WANG, Tao WANG, Sheng-jin TANG, Yan-zhao SU. Hyperspectral Anomaly Detection Based on 3D Convolutional Autoencoder Network[J]. Spectroscopy and Spectral Analysis, 2022, 42(4): 1270
Paper Information
Category: Research Articles
Received: Mar. 13, 2021
Accepted: --
Published Online: Jul. 25, 2023
The Author Email: WANG Sheng-ming (210279598@qq.com)
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