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SAR U-Net: Spatial attention residual U-Net structure for water body segmentation from remote sensing satellite images

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Abstract

The analysis of remote-sensing images always requires the extraction of data about the aquatic environment. However, it might be challenging to identify any water surface since the backgrounds of water zones in remote sensing images are usually complicated structures and dense vegetation. Furthermore, less significant tributaries and edge data could not be accurately detected using traditional water detection methods. As a result, a spatial attention residual U-Net architecture is proposed to enhance the effectiveness of water body segmentation. The suggested approach reweights the feature representation spatially to obtain data on water features, using U-Net as the network architecture. The feature of the water zone is obtained using the residual block. It obtains more precise local position data for the water zone, which enhance edge segmentation accuracy. The spatial attention module retrieves, segregates, and combines the low-level information and high-level information as two discrete inputs in various dimensions. To effectively segregate the water region from the context, the spatial attention module combines spatial features with deep contextual information. The experiments are performed using satellite images of kaggle dataset aquatic bodies and a real-time dataset. The results of the experiments reveal 96% of accuracy that the suggested strategy out performs the existing models.

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https://www.kaggle.com/franciscoescobar/satellite-images-of-water-bodies

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Authors and Affiliations

  1. School of Electronics Engineering, VIT-AP University, Amaravati, AP, 522237, India

    Naga Surekha Jonnala & Neha Gupta

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  1. Naga Surekha Jonnala
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  2. Neha Gupta
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Correspondence to Neha Gupta.

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Jonnala, N.S., Gupta, N. SAR U-Net: Spatial attention residual U-Net structure for water body segmentation from remote sensing satellite images. Multimed Tools Appl 83, 44425–44454 (2024). https://doi.org/10.1007/s11042-023-16965-8

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