{"title":"Spatial Reduction Attention in Multiscale Vision Transform for Surface Water–Land Interface Zone Segmentation","authors":"Yu-Hsuan Chen;Trong-An Bui;Pei-Jun Lee;Ching-Huo Hsu","doi":"10.1109/JSTARS.2024.3455891","DOIUrl":null,"url":null,"abstract":"Water segmentation is important for applications in flood prevention, water resource management, and urban planning. The accurate identification of water–land interface zones and the delineation of edges between water and land in remote sensing satellite imagery, however, present significant challenges for traditional segmentation methods. This research aims to enhance the precision of segmentation, particularly in identifying water and land interface zones, while also reducing computational demands to enable real-time analysis on edge devices. This article introduces a novel spatial reduction attention (SRA) mechanism within the multiscale vision transform framework, which is proficient at capturing both local and global features. The proposed multiscale multihead attention mechanism, enhanced with multiscale projection and SRA, aids in learning features from various receptive fields, thereby increasing computational efficiency. The integration of dual-branch channels for multispectral imagery and color attributes significantly improves the model's recognition capabilities. In the evaluation of water segmentation, the proposed method significantly outperforms advanced models, achieving a 10.1% improvement in mean intersection over union and a 6.7% increase in mean \n<italic>F</i>\n1-score. This performance underscores the model's efficacy in accurately identifying water–land interface zones and highlights its potential in improving both the accuracy and efficiency of water segmentation in satellite imagery.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669118","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10669118/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Water segmentation is important for applications in flood prevention, water resource management, and urban planning. The accurate identification of water–land interface zones and the delineation of edges between water and land in remote sensing satellite imagery, however, present significant challenges for traditional segmentation methods. This research aims to enhance the precision of segmentation, particularly in identifying water and land interface zones, while also reducing computational demands to enable real-time analysis on edge devices. This article introduces a novel spatial reduction attention (SRA) mechanism within the multiscale vision transform framework, which is proficient at capturing both local and global features. The proposed multiscale multihead attention mechanism, enhanced with multiscale projection and SRA, aids in learning features from various receptive fields, thereby increasing computational efficiency. The integration of dual-branch channels for multispectral imagery and color attributes significantly improves the model's recognition capabilities. In the evaluation of water segmentation, the proposed method significantly outperforms advanced models, achieving a 10.1% improvement in mean intersection over union and a 6.7% increase in mean
F
1-score. This performance underscores the model's efficacy in accurately identifying water–land interface zones and highlights its potential in improving both the accuracy and efficiency of water segmentation in satellite imagery.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.