The identification of floods from Sentinel-1 data based on the combination of difference images obtained from different radar polarizations.

mohammadzadeh, ali; asadi, saeed

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Volume 14, Issue 4 (6-2025)

JGST 2025, 14(4): 31-49

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The identification of floods from Sentinel-1 data based on the combination of difference images obtained from different radar polarizations.

Ali Mohammadzadeh ^*

, Saeed Asadi

Abstract: (72 Views)

With the onset of climate change, which indicates the recurrence of floods in many parts of the world, and the continuous increase of economic assets in flood-prone areas, the need for community protection against floods has risen. Optical images have previously been successfully used to extract flood-prone areas; however, in situations where weather conditions are cloudy and unsuitable, preventing optical sensors from performing imaging operations, the use of radar data as a complement can be appropriate. In this research, a time series of Sentinel-1 images was utilized within the Google Earth Engine framework for rapid flood identification. The flood in April 2019 in Khuzestan Province affected an area of 1,198 square kilometers, causing significant damage to various residential and agricultural sectors. This study explored flooding in the Ahvaz and Shakariyeh regions by employing satellite images and presenting a flood detection algorithm. In the proposed method, the difference image was first calculated using the two bands VV and VH, and then the logarithmic ratio images with the two bands VV and VH were also produced to utilize the information from the two different bands of radar images to reduce noise in the image. Subsequently, wavelet transformation was combined with the averaging method of the corresponding coefficients from the two images to merge the difference image with the logarithmic ratio images separately for both bands VV and VH, thereby enhancing flood identification performance by integrating the data obtained from the two different polarizations, VV and VH. The merged image with two bands was then divided into M non-overlapping blocks of dimensions h×h, which were utilized in the PCA-K-means algorithm to produce a binary flood map, thus improving the performance of the conventional PCA-K-means algorithm. Comparing the proposed method in two scenarios using the conventional PCA-K-means model and the improved version demonstrated a superiority of the enhanced PCA-K-means model by 7% in F-score accuracy rate for the first dataset and by 6% for the second dataset compared to the conventional method. Moreover, the proposed method outperformed the PCA-GMM approach, increasing the F-score accuracy rate by 23% and 6% for the first and second datasets, respectively. The results obtained for the study areas of Ahvaz and Shakariyeh indicated overall accuracies of 95% and 94%, respectively. Based on the results obtained, it can be concluded that the method introduced in this research has a high capability for flood detection.

Article number: 3

Keywords: Flood Mapping, Kmeans, Wavelet fusion, Logarithmic ratios, Sentinel-1

Full-Text [PDF 1981 kb] (29 Downloads)

Type of Study: Research | Subject: Photo&RS
Received: 2024/07/12

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mohammadzadeh A, asadi S. The identification of floods from Sentinel-1 data based on the combination of difference images obtained from different radar polarizations.. JGST 2025; 14 (4) : 3
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Volume 14, Issue 4 (6-2025)

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