Surface water extraction from remote sensing images of arid regions in Africa using a deep learning approach combining multiscale information

Yong Li; Xiuhui Liu

doi:10.1117/12.3045960

20 November 2024 Surface water extraction from remote sensing images of arid regions in Africa using a deep learning approach combining multiscale information

Yong Li, Xiuhui Liu

Author Affiliations +

Proceedings Volume 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI; 131910A (2024) https://doi.org/10.1117/12.3045960
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

Surface water is of critical importance to the ecosystem, agricultural production and livelihoods of people in the arid regions of Africa. The surface water is characterized by significant differences in spectral features, complex morphological changes, and more fine streams, which reflects the climate and water resource conditions in Africa. To address the spectral and spatial variability of surface water of large areas, this paper proposes a new surface water extraction model. The model utilizes a VGG network to extract local information, while an MVT module is introduced to capture global contextual information in remote sensing images. Spatial attention and channel attention mechanisms are used to effectively fuse global and local information to more accurately recognize and extract continuous spatial relationships of water bodies. In this paper, Sentinel-2 multispectral remote sensing images are used to finely extract surface water in Egypt during the dry season using the proposed model. Experimental results show that the model not only improves the accuracy of fine water extraction, but also performs well in waters with complex backgrounds and boundaries.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yong Li and Xiuhui Liu "Surface water extraction from remote sensing images of arid regions in Africa using a deep learning approach combining multiscale information", Proc. SPIE 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI, 131910A (20 November 2024); https://doi.org/10.1117/12.3045960

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE