{"title":"AFUNet With Active Contour Loss for Water Body Detection in SAR Imagery","authors":"Bin Han;Guangao Xing;Xiaozhen Lu;Anup Basu","doi":"10.1109/JSTARS.2024.3459624","DOIUrl":null,"url":null,"abstract":"With advancements in remote sensing technology, synthetic aperture radar (SAR) imagery has become one of the main methods to detect surface water bodies. The detection of water bodies in SAR imagery remains a challenging task due to the presence of complex interference. To achieve accurate water body detection, we proposed an attention fusion U-net inspired by the effectiveness of U-net in segmenting small targets with weak edges. First, the spatial attention module and channel attention module are added to the skip connections between encoder and decoder parts to extract useful low- and high-level features, thereby compensating for the loss of semantic information of downsampling. Second, the multiscale convolutional pooling block is introduced into the encoder part to better utilize the contextual information, capturing water and land features at different scales. Third, considering the feature distortion resulting from upsampling, an attentional upsampler (AU) is designed to facilitate lossless feature fusion. Furthermore, an active contour loss is designed as additional regularization to learn more boundary information, improving the model's segmentation performance. The water body detection experiments on the ALOS phased array L-band SAR and Sen1-SAR datasets demonstrate that the presented AFUNet outperforms the related start-of-the-art methods in detection accuracy in terms of five evaluation metrics.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10678920","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/10678920/","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
With advancements in remote sensing technology, synthetic aperture radar (SAR) imagery has become one of the main methods to detect surface water bodies. The detection of water bodies in SAR imagery remains a challenging task due to the presence of complex interference. To achieve accurate water body detection, we proposed an attention fusion U-net inspired by the effectiveness of U-net in segmenting small targets with weak edges. First, the spatial attention module and channel attention module are added to the skip connections between encoder and decoder parts to extract useful low- and high-level features, thereby compensating for the loss of semantic information of downsampling. Second, the multiscale convolutional pooling block is introduced into the encoder part to better utilize the contextual information, capturing water and land features at different scales. Third, considering the feature distortion resulting from upsampling, an attentional upsampler (AU) is designed to facilitate lossless feature fusion. Furthermore, an active contour loss is designed as additional regularization to learn more boundary information, improving the model's segmentation performance. The water body detection experiments on the ALOS phased array L-band SAR and Sen1-SAR datasets demonstrate that the presented AFUNet outperforms the related start-of-the-art methods in detection accuracy in terms of five evaluation metrics.
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.