RT info:eu-repo/semantics/article
T1 Water detection in satellite images based on fractal dimension
A1 Del Pozo Velázquez, Javier
A1 Chamorro Posada, Pedro
A1 Aguiar Pérez, Javier Manuel
A1 Pérez Juárez, María Ángeles
A1 Casaseca de la Higuera, Juan Pablo
K1 Image segmentation
K1 Fractal dimension
K1 OpenAerialMap
K1 Quadtree
K1 Satellite images
K1 Water detection
K1 33 Ciencias Tecnológicas
K1 25 Ciencias de la Tierra y del Espacio
AB 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.
PB MDPI
SN 2504-3110
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/64801
UL https://uvadoc.uva.es/handle/10324/64801
LA eng
NO Fractal and Fractional, Noviembre 2022, vol. 6, n. 11. p. 657
NO Producción Científica
DS UVaDOC
RD 22-nov-2024