Author:
Banerjee Sriparna,Sinha Chaudhuri Sheli,Mehra Raghav,Misra Arundhati
Reference21 articles.
1. De, S., Bruzzone, L., Bhattacharya, A., Bovolo, F., Chaudhuri, S.: A novel technique based on deep learning and a synthetic target database for classification of urban areas in PolSAR data. IEEE J. Select. Topics Appl. Earth Obser. Remote Sens. 11(1), 154–170 (2017)
2. Chen, S-W., Tao, C.-S.: PolSAR image classification using polarimetric-feature-driven deep convolutional neural network. IEEE Geosci. Remote Sens. Lett. 15(4), 627–631 (2018)
3. Guo, J., Wei, P.L., Liu, J., Jin, B., Su, B.F., Zhou, Z.S.: Crop classification based on differential characteristics of H/α scattering parameters for multitemporal quad- and dual-polarization sar images. IEEE Trans. Geosci. Remote Sens. 56(10), 6111–6123 (2018)
4. Mei, X., Nie, W., Liu, J., Huang, K.: PolSAR image crop classification based on deep residual learning network. In: 7th International Conference on Agro-Geoinformatics, pp. 1–6. IEEE, China (2018)
5. https://crisp.nus.edu.sg/~research/tutorial/sar_int.htm
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