| A CONVOLUTIONAL NEURAL NETWORK FOR FLOOD MAPPING USING SENTINEL-1 AND SRTM DEM DATA: A CASE STUDY IN POL-E-DOKHTAR-IRAN |
| Paper ID : 1346-SMPR |
| Authors: |
|
Benyamin Hosseiny *1, Nafiseh Ghasemian Sorboni2, Jalal Amini3 1Department of Geospatial engineering, Faculty of Engineering, University of Tehran, Tehran, Iran 2School of Surveying and Geospatial Engineering, College of Eng., University of Tehran, Tehran, Iran 3School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran |
| 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. After classifying the full scene, water inundated areas are estimated based on a binary change map. |
| Keywords: |
| Flood, Convolutional Neural networks, Classification, Radar image, Synthetic aperture radar, Sentinel-1, Digital Elevation Model |
| Status : Conditional Accept (Oral Presentation) |
