Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks
Abstract
:1. Introduction
2. Related Work
3. Methodology
3.1. Adaptive CNN Implementation
3.2. Back-Propagation for Adaptive CNNs
4. Experimental Results
4.1. Benchmark SAR Data
4.1.1. Po Delta, COSMO-SkyMed, and X-Band
4.1.2. Dresden, TerraSAR-X, and X-Band
4.2. Experimental Setup
4.3. Results and Performance Evaluations
4.3.1. Performance Evaluations over Po Delta Data
4.3.2. Performance Evaluations over Dresden Data
4.3.3. Deep versus Compact CNNs
4.4. Sensitivity Analysis on Hyper-Parameters
4.5. Computational Complexity
5. Conclusions and Future Works
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Appendix A.1. Adaptive CNN Implementation
Appendix A.2. Back-Propagation for Adaptive CNNs
Appendix A.2.1. Inter-BP among CNN Layers:
Appendix A.2.2. Intra-BP within a CNN Neuron:
Appendix A.2.3. BP from the First MLP Layer to the Last Convolutional Layer
Appendix A.2.4. Computation of the Weight (Kernel) and Bias Sensitivities
- (1)
- Initialize weights (kernels) and biases (e.g., randomly, U(−0.5, 0.5)) of the CNN.
- (2)
- For each BP iteration (t=1:iterNo) DO:
- For each patch, p, in the train set, DO:
- FP: Forward propagate from the input layer to the output layer to find output of each neuron at each layer, [1,L].
- BP: Compute delta error at the output (MLP) layer and back-propagate it to first hidden CNN layer to compute the delta errors, .
- PP: Post-process the delta error to obtain the weight and bias sensitivities using Equations (A14) and (A15).
- Update: Cumulate the sensitivities in iii and scale with the learning factor, ε, and update the weights and biases as follows:
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Name | System and Band | Date | Incident Angles | Mode |
---|---|---|---|---|
Po Delta | COSMO-SkyMed, (X-band) | September 2007 | 30° | Single |
Dresden | TerraSAR-X, (X-band) | Feburary 2008 | 41–42° | Dual |
Name | Dimensions | # Class | Samples in Training per Class | Total Samples in GTD |
---|---|---|---|---|
Po Delta | 6 | 2000 | 612,000 | |
Dresden | 6 | 1000 | 606,000 |
Po Delta (COSMO-SkyMed) | 1-channel | 4-channels |
---|---|---|
Window Size | ||
5 × 5 | 0.7098 | 0.708 |
7 × 7 | 0.7482 | 0.7501 |
9 × 9 | 0.7698 | 0.7668 |
11 × 11 | 0.789 | 0.7838 |
13 × 13 | 0.8075 | 0.8037 |
15 × 15 | 0.8147 | 0.8167 |
17 × 17 | 0.8276 | 0.83 |
19 × 19 | 0.8387 | 0.8442 |
21 × 21 | 0.8404 | 0.8537 |
23 × 23 | 0.848 | 0.8539 |
25 × 25 | 0.8487 | 0.8632 |
27 × 27 | 0.8533 | 0.8615 |
Predicted | ||||||||
---|---|---|---|---|---|---|---|---|
Urban | InWater | Forest | Wetland | Water | Crop | Total | ||
True | Urban | 92,264 | 607 | 1322 | 54 | 0 | 5753 | 100,000 |
InWater | 931 | 85,308 | 3824 | 6781 | 1210 | 1946 | 100,000 | |
Forest | 934 | 2581 | 90,507 | 909 | 186 | 4883 | 100,000 | |
Wetland | 166 | 6153 | 1157 | 80,683 | 11,744 | 97 | 100,000 | |
MaWater | 48 | 2196 | 166 | 17,502 | 80,067 | 21 | 100,000 | |
Crop | 4680 | 1055 | 4875 | 253 | 52 | 89,085 | 100,000 | |
Total | 99,023 | 97,900 | 101,851 | 106,182 | 93,259 | 101,785 | 517,914 |
Dresden (TerraSAR-X) | 2-channel | ||
---|---|---|---|
Window Size | Window Size | ||
5 × 5 | 0.7059 | 17 × 17 | 80.07 |
7 × 7 | 0.7509 | 19 × 19 | 0.8105 |
9 × 9 | 0.7654 | 21 × 21 | 0.8133 |
11 × 11 | 0.7797 | 23 × 23 | 0.8029 |
13 × 13 | 0.7898 | 25 × 25 | 0.8092 |
15 × 15 | 0.798 | 27 × 27 | 0.8062 |
Predicted | ||||||||
---|---|---|---|---|---|---|---|---|
Urban | Industrial | InWater | Forest | Pastures | Crop | Total | ||
True | Urban | 73,409 | 18,980 | 169 | 3323 | 1775 | 2344 | 100,000 |
Industrial | 16,492 | 78,870 | 172 | 1003 | 415 | 3048 | 100,000 | |
InWater | 1474 | 1192 | 93,012 | 1955 | 2182 | 185 | 100,000 | |
Forest | 3081 | 1189 | 855 | 90,712 | 2193 | 1970 | 100,000 | |
Pastures | 3961 | 1199 | 977 | 3863 | 73,175 | 16,825 | 100,000 | |
Crop | 2895 | 1035 | 113 | 1337 | 15,802 | 78,818 | 100,000 | |
Total | 101,312 | 102,465 | 95,298 | 102,193 | 95,542 | 103,190 | 487,996 |
Window Size | Xception | Xception* | Inception ResNet-v2 | Inception ResNet-v2* | Proposed |
---|---|---|---|---|---|
5 × 5 | 0.6688 | 0.6963 | 0.6862 | 0.6928 | 0.708 |
11 × 11 | 0.7563 | 0.7736 | 0.7608 | 0.7656 | 0.7838 |
17 × 17 | 0.7896 | 0.7943 | 0.8032 | 0.8121 | 0.83 |
25 × 25 | 0.8445 | 0.8435 | 0.8555 | 0.8447 | 0.8632 |
Window Size | Xception | Xception* | Inception ResNet-v2 | Inception ResNet-v2* | Proposed |
---|---|---|---|---|---|
5 × 5 | 0.4637 | 0.5004 | 0.4657 | 0.4754 | 0.7059 |
11 × 11 | 0.6481 | 0.6836 | 0.6556 | 0.6488 | 0.7797 |
17 × 17 | 0.7342 | 0.7596 | 0.7441 | 0.7459 | 0.8007 |
21 × 21 | 0.7706 | 0.7783 | 0.7767 | 0.7796 | 0.8133 |
27 × 27 | 0.7960 | 0.8068 | 0.8116 | 0.8064 | 0.8062 |
Po Delta (Classes) | Precision Xcep. Inc. Res2. Prop. | Recall Xcep. Inc. Res2. Prop. | F1 Score Xcep. Inc. Res2. Prop. | ||||||
---|---|---|---|---|---|---|---|---|---|
Urban | 0.9617 | 0.7246 | 0.9317 | 0.9631 | 0.7341 | 0.9226 | 0.9624 | 0.7293 | 0.9272 |
InWater | 0.8091 | 0.7697 | 0.8714 | 0.8907 | 0.7887 | 0.8531 | 0.8479 | 0.7791 | 0.8621 |
Forest | 0.902 | 0.9760 | 0.8886 | 0.9244 | 0.9301 | 0.9051 | 0.9131 | 0.9525 | 0.8968 |
Wetland | 0.7014 | 0.8877 | 0.7599 | 0.6956 | 0.9071 | 0.8068 | 0.6985 | 0.8973 | 0.7826 |
MaWater | 0.7903 | 0.7659 | 0.8585 | 0.7053 | 0.7318 | 0.8007 | 0.7454 | 0.7484 | 0.8286 |
Araple Land | 0.8939 | 0.7638 | 0.8752 | 0.8838 | 0.7882 | 0.8909 | 0.8888 | 0.7758 | 0.8830 |
Dresden (Classes) | Precision Xcep Inc. Res2. Prop. | Recall Xcep. Inc. Res2. Prop. | F1 Score Xcep. Inc. Res2. Prop. | ||||||
---|---|---|---|---|---|---|---|---|---|
Urban | 0.6387 | 0.6875 | 0.7246 | 0.6144 | 0.5883 | 0.7341 | 0.6263 | 0.6341 | 0.7293 |
Industrial | 0.7116 | 0.7136 | 0.7697 | 0.6608 | 0.7349 | 0.7887 | 0.6852 | 0.7241 | 0.7791 |
InWater | 0.999 | 0.9995 | 0.9760 | 0.965 | 0.9743 | 0.9301 | 0.9817 | 0.9867 | 0.9525 |
Forest | 0.8412 | 0.8426 | 0.8877 | 0.9022 | 0.9067 | 0.9071 | 0.8706 | 0.8735 | 0.8973 |
Pastures | 0.791 | 0.8025 | 0.7659 | 0.7933 | 0.8259 | 0.7318 | 0.7921 | 0.8141 | 0.7484 |
Araple Land | 0.7868 | 0.8112 | 0.7638 | 0.8404 | 0.8391 | 0.7882 | 0.8127 | 0.8249 | 0.7758 |
SAR Data | Xception | Inception ResNet-v2 | Proposed |
---|---|---|---|
Po Delta | 0.7203 | 0.7278 | 0.7369 |
Dresden | 0.6828 | 0.6950 | 0.6986 |
Po Delta (HH) | Dresden (VH/VV) | |
---|---|---|
m = 1, n = 1 | 0.8487 | 0.8133 |
m = 1, n = 2 | 0.8015 | 0.7772 |
m = 2, n = 1 | 0.8493 | 0.8176 |
m = 2, n = 2 | 0.7945 | 0.7747 |
m = 4, n = 1 | 0.8683 | 0.8371 |
m = 4, n = 2 | 0.7908 | 0.7766 |
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Ahishali, M.; Kiranyaz, S.; Ince, T.; Gabbouj, M. Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks. Remote Sens. 2019, 11, 1340. https://doi.org/10.3390/rs11111340
Ahishali M, Kiranyaz S, Ince T, Gabbouj M. Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks. Remote Sensing. 2019; 11(11):1340. https://doi.org/10.3390/rs11111340
Chicago/Turabian StyleAhishali, Mete, Serkan Kiranyaz, Turker Ince, and Moncef Gabbouj. 2019. "Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks" Remote Sensing 11, no. 11: 1340. https://doi.org/10.3390/rs11111340