2026-04-27T22:10:17Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/648012024-01-22T20:01:49Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

00925njm 22002777a 4500 dc Del Pozo Velázquez, Javier author Chamorro Posada, Pedro author Aguiar Pérez, Javier Manuel author Pérez Juárez, María Ángeles author Casaseca de la Higuera, Juan Pablo author 2022 Identification and monitoring of existing surface water bodies on the Earth are important in many scientific disciplines and for different industrial uses. This can be performed with the help of high-resolution satellite images that are processed afterwards using data-driven techniques to obtain the desired information. The objective of this study is to establish and validate a method to distinguish efficiently between water and land zones, i.e., an efficient method for surface water detection. In the context of this work, the method used for processing the high-resolution satellite images to detect surface water is based on image segmentation, using the Quadtree algorithm, and fractal dimension. The method was validated using high-resolution satellite images freely available at the OpenAerialMap website. The results show that, when the fractal dimensions of the tiles in which the image is divided after completing the segmentation phase are calculated, there is a clear threshold where water and land can be distinguished. The proposed scheme is particularly simple and computationally efficient compared with heavy artificial-intelligence-based methods, avoiding having any special requirements regarding the source images. Moreover, the average accuracy obtained in the case study developed for surface water detection was 96.03%, which suggests that the adopted method based on fractal dimension is able to detect surface water with a high level of accuracy. Fractal and Fractional, Noviembre 2022, vol. 6, n. 11. p. 657 2504-3110 https://uvadoc.uva.es/handle/10324/64801 10.3390/fractalfract6110657 657 11 Fractal and Fractional 6 2504-3110 Water detection in satellite images based on fractal dimension