Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 527–533, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-527-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 527–533, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-527-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

A CONVOLUTIONAL NEURAL NETWORK FOR FLOOD MAPPING USING SENTINEL-1 AND SRTM DEM DATA: CASE STUDY IN POLDOKHTAR-IRAN

B. Hosseiny, N. Ghasemian, and J. Amini B. Hosseiny et al.
  • School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Keywords: Change detection, Deep learning, Classification, Satellite images, Synthetic aperture radar

Abstract. Flood contributes a key role in devastating natural and man-made areas. Floods usually are occurred when there is a considerable number of clouds in the sky making optic data useless. Synthetic aperture radar (SAR) images can be a valuable data source in earth observation tasks. The most important characteristic of the radar image is its ability to penetrate the cloud and dust. Therefore, monitoring earth in cloudy or rainy weather can be available by this kind of dataset. In the last few years by improving machine learning methods and development of convolutional neural networks in remote sensing applications we are facing with extremely high improvement in classification tasks. In this paper, we use dual-polarized VV and VH backscatter values of Sentinel-1 and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) dataset in a proposed convolutional neural network to generate a land cover map of a flooded area before and after happening. Obtained classification results vary between 93.3% to 98.5% for different training sizes. By comparing the generated classified maps, flooded areas of each class can be extracted.