Scattering Transform Framework for Unmixing of Hyperspectral Data
Abstract
:1. Introduction
- (1)
- The framework based on scattering transform is firstly employed to solve the hyperspectral unmixing problem. The STFHU framework extracts the high-level information by using a multilayer network to decompose the hyperspectrum and then utilizes the k-NN regressor to relate the feature vectors to their abundances.
- (2)
- The proposed method can obtain equivalent performance using less training samples than CNN-based approaches. Meanwhile, the parameter setting of the scattering transform framework is less complicated than that of the CNN.
- (3)
- The scattering transform features are well suited to eliminate effects of Gaussian white noise. The model trained using non-noisy data can also achieve satisfactory unmixing results when being applied to noisy data.
2. Methodology
2.1. Pixel-Based Wavelet Scattering Transform
2.2. 3D-Based Scattering Transform
2.3. Regression Model of Scattering Transform Features
3. Experimental Results and Analysis
3.1. Experimental Datasets
3.1.1. Synthetic Hyperspectral Dataset
3.1.2. Real-World Hyperspectral Datasets
- (1)
- Urban data
- (2)
- Jasper Ridge data
- (3)
- Samson data
3.2. Experimental Setup
3.3. Experimental Results for the Synthetic Hyperspectral Data
3.3.1. Noise Data Results
3.3.2. Results When Using Different Proportions of Samples Used for Training
3.4. Experimental Results for Real-World Hyperspectral Data
3.4.1. Results of Experiment Based on the Urban Dataset
3.4.2. Results of Experiments Based on the Jasper Ridge and Samson Datasets
4. Discussion
- (1)
- Robustness to noise
- (2)
- Effect of limited training samples
- (3)
- Discussion of computational complexity
- (4)
- Discussion of the preliminary 3D-based STFHU results
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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EM-1 | EM-2 | EM-3 | EM-4 | EM-5 | EM-6 | EM-7 | EM-8 | Avg. | Rms-AAD | |
---|---|---|---|---|---|---|---|---|---|---|
Original | 0.0120 | 0.0169 | 0.0121 | 0.0140 | 0.0139 | 0.0190 | 0.0176 | 0.0127 | 0.0148 | 0.0688 |
Noise1 | 0.0441 | 0.0458 | 0.0415 | 0.0411 | 0.0379 | 0.0482 | 0.0424 | 0.0364 | 0.0422 | 0.2239 |
Noise2 | 0.0937 | 0.0910 | 0.0929 | 0.0690 | 0.1114 | 0.1061 | 0.0820 | 0.0693 | 0.0894 | 0.4655 |
Training Ratio | Methods | EM-1 | EM-2 | EM-3 | EM-4 | EM-5 | EM-6 | EM-7 | EM-8 | Avg. | Rms-AAD |
---|---|---|---|---|---|---|---|---|---|---|---|
10% | STFHU | 0.0278 | 0.0370 | 0.0297 | 0.0329 | 0.0301 | 0.0422 | 0.0410 | 0.0309 | 0.0340 | 0.1524 |
CNN | 0.0500 | 0.0653 | 0.0655 | 0.0680 | 0.0558 | 0.0569 | 0.0597 | 0.0671 | 0.0610 | 0.1543 | |
ANN | 0.0391 | 0.0372 | 0.0417 | 0.0458 | 0.0493 | 0.0602 | 0.0333 | 0.0305 | 0.0421 | 0.1647 | |
5% | STFHU | 0.0347 | 0.0455 | 0.0367 | 0.0376 | 0.0374 | 0.0489 | 0.0482 | 0.0371 | 0.0408 | 0.1804 |
CNN | 0.0590 | 0.0922 | 0.0866 | 0.0852 | 0.0591 | 0.0990 | 0.0833 | 0.0577 | 0.0778 | 0.1994 | |
ANN | 0.0563 | 0.0521 | 0.0507 | 0.0607 | 0.0800 | 0.0783 | 0.0597 | 0.0452 | 0.0604 | 0.2342 |
50% | 10% | 5% | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
STFHU | CNN | LSU | ANN | STFHU | CNN | LSU | ANN | STFHU | CNN | LSU | ANN | |
Road | 0.0430 | 0.0487 | 0.1264 | 0.1950 | 0.1022 | 0.0900 | 0.1893 | 0.2519 | 0.1140 | 0.1372 | 0.1965 | 0.2643 |
Grass | 0.0365 | 0.0625 | 0.1303 | 0.1512 | 0.1018 | 0.0814 | 0.1907 | 0.3619 | 0.1162 | 0.0947 | 0.1937 | 0.3693 |
Tree | 0.0241 | 0.0467 | 0.1547 | 0.1713 | 0.0659 | 0.0784 | 0.2839 | 0.2415 | 0.0750 | 0.1035 | 0.3484 | 0.2358 |
Roof | 0.0150 | 0.0321 | 0.1253 | 0.1211 | 0.0319 | 0.0470 | 0.2174 | 0.1275 | 0.0353 | 0.0936 | 0.3241 | 0.1349 |
Metal | 0.0231 | 0.0380 | 0.0795 | 0.0896 | 0.0403 | 0.1153 | 0.1196 | 0.1178 | 0.0559 | 0.1188 | 0.1240 | 0.1204 |
Dirt | 0.0388 | 0.0456 | 0.0826 | 0.1211 | 0.0702 | 0.0785 | 0.1074 | 0.1296 | 0.0770 | 0.1060 | 0.1125 | 0.1368 |
Avg. | 0.0301 | 0.0456 | 0.1165 | 0.1415 | 0.0688 | 0.0818 | 0.1847 | 0.205 | 0.0790 | 0.1090 | 0.2166 | 0.2103 |
Methods | STFHU | CNN | LSU | ANN |
---|---|---|---|---|
Tree | 0.0141 | 0.0291 | 0.0360 | 0.0812 |
Water | 0.0110 | 0.0251 | 0.0268 | 0.1147 |
Soil | 0.0301 | 0.0437 | 0.0476 | 0.1166 |
Road | 0.0306 | 0.0431 | 0.0409 | 0.0965 |
Avg. | 0.0215 | 0.0353 | 0.0378 | 0.1023 |
Methods | STFHU | CNN | LSU | ANN |
---|---|---|---|---|
Rock | 0.0201 | 0.0542 | 0.0501 | 0.1382 |
Tree | 0.0172 | 0.0532 | 0.0500 | 0.1261 |
Water | 0.0077 | 0.0255 | 0.0330 | 0.2024 |
Avg. | 0.0150 | 0.0443 | 0.0444 | 0.1556 |
Training Ratio | 2% | 1% | 0.5% | 0.3% | 0.1% | |
---|---|---|---|---|---|---|
Avg-RMSE | Pixel | 0.1534 | 0.1534 | 0.2148 | 0.2458 | 0.2990 |
3D | 0.1407 | 0.1442 | 0.1584 | 0.1912 | 0.2460 | |
rms-AAD | Pixel | 0.4738 | 0.4738 | 0.6788 | 0.7564 | 0.9489 |
3D | 0.4369 | 0.4524 | 0.4910 | 0.5860 | 0.7588 |
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Zeng, Y.; Ritz, C.; Zhao, J.; Lan, J. Scattering Transform Framework for Unmixing of Hyperspectral Data. Remote Sens. 2019, 11, 2868. https://doi.org/10.3390/rs11232868
Zeng Y, Ritz C, Zhao J, Lan J. Scattering Transform Framework for Unmixing of Hyperspectral Data. Remote Sensing. 2019; 11(23):2868. https://doi.org/10.3390/rs11232868
Chicago/Turabian StyleZeng, Yiliang, Christian Ritz, Jiahong Zhao, and Jinhui Lan. 2019. "Scattering Transform Framework for Unmixing of Hyperspectral Data" Remote Sensing 11, no. 23: 2868. https://doi.org/10.3390/rs11232868
APA StyleZeng, Y., Ritz, C., Zhao, J., & Lan, J. (2019). Scattering Transform Framework for Unmixing of Hyperspectral Data. Remote Sensing, 11(23), 2868. https://doi.org/10.3390/rs11232868