[1] Benyamin Hosseiny, Nafise Ghasemian, Jalal Amini, 2019. A Convolutional Neural Network For Flood Mapping Using Sentinel-1 And SRTM Dem Data: Case Study In Poldokhtar-Iran The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W18, 2019 GeoSpatial Conference 2019 – Joint Conferences of SMPR and GI Research, 12–14 October 2019, Karaj, Iran.

[2] Domeneghetti, A., Schumann, G.J.P. and Tarpanelli, A., 2019. Preface: Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics.

[3] Gao, F., Dong, J., Li, B. and Xu, Q., 2016. Automatic change detection in synthetic aperture radar images based on PCANet. IEEE Geoscience and Remote Sensing Letters, 13(12), pp.1792- 1796.

[4] Gong, M., Yang, H. and Zhang, P., 2017. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images. ISPRS Journal of Photogrammetry and Remote Sensing, 129, pp.212-225.

[5] Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep learning. MIT press.

[6] Kang, W., Xiang, Y., Wang, F., Wan, L. and You, H., 2018. Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks. Sensors, 18(9), p.2915.

[7] Khan, S., Rahmani, H., Shah, S.A.A. and Bennamoun, M., 2018. A guide to convolutional neural networks for computer vision. Synthesis Lectures on Computer Vision, 8(1), pp.1-207.

[8] Liu, J., Gong, M., Qin, K. and Zhang, P., 2016. A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE transactions on neural networks and learning systems, 29(3), pp.545-559.