A Spatial Semantics and Continuity Perception Attention for Remote Sensing Water Body Change Detection
Quanqing Ma, Jiaen Chen, Peng Wang, Yao Zheng, Qingzhan Zhao, Yuchen Zheng

TL;DR
This paper introduces a new high-resolution dataset and a novel attention module, SSCP, to improve water body change detection in remote sensing images by better exploiting spatial semantics and structural information.
Contribution
The paper presents a new high-resolution dataset HSRW-CD and a plug-and-play SSCP attention module that enhances deep feature discrimination for water body change detection.
Findings
The SSCP module significantly improves detection accuracy.
The HSRW-CD dataset covers diverse water body types.
Experiments validate the effectiveness and generalization of SSCP.
Abstract
Remote sensing Water Body Change Detection (WBCD) aims to detect water body surface changes from bi-temporal images of the same geographic area. Recently, the scarcity of high spatial resolution datasets for WBCD restricts its application in urban and rural regions, which require more accurate positioning. Meanwhile, previous deep learning-based methods fail to comprehensively exploit the spatial semantic and structural information in deep features in the change detection networks. To resolve these concerns, we first propose a new dataset, HSRW-CD, with a spatial resolution higher than 3 meters for WBCD. Specifically, it contains a large number of image pairs, widely covering various water body types. Besides, a Spatial Semantics and Continuity Perception (SSCP) attention module is designed to fully leverage both the spatial semantics and structure of deep features in the WBCD networks,…
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Taxonomy
TopicsFlood Risk Assessment and Management · Remote-Sensing Image Classification · Automated Road and Building Extraction
