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High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network

Zhang, Ziwen; Li, Yang; Liu, Qi; Liu, Xiaodong

Authors

Ziwen Zhang

Yang Li

Qi Liu



Abstract

A basic stage of hydrological research is to automatically extract water body information from high-resolution remote sensing images. Various methods based on deep learning convolutional neural networks have been proposed in recent studies to achieve segmentation. Such as FCN, PSPNet, Unet and so on. However, due to the complexity and multi-scale nature of high-resolution images, traditional segmentation networks cannot classify each pixel in the image well because they do not consider the spatial context information of the overall image, therefore, the results of segmentation often appear defects such as rough edges and inadequate water integrity.Based on the above reasons, this paper designs a two-way segmentation network DBAN based on spatial attention, which fully considers the detailed information and spatial context information of the image to refine the segmentation results.

Presentation Conference Type Conference Paper (Published)
Conference Name 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Start Date Sep 12, 2022
End Date Sep 15, 2022
Acceptance Date Jul 25, 2022
Online Publication Date Dec 13, 2022
Publication Date 2022
Deposit Date Jan 27, 2023
Publisher Institute of Electrical and Electronics Engineers
Book Title 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
DOI https://doi.org/10.1109/dasc/picom/cbdcom/cy55231.2022.9927756