AEDNet: An Attention-Based Encoder-Decoder Network for Urban Water Extraction From High Spatial Resolution Remote Sensing Images

Accurate water extraction from urban remote sensing images holds great significance in assisting the formulation of river and lake management policies and ensuring the sustainable development of urban water resources. However, urban high-resolution remote sensing images encompass complex spatial and...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 17; S. 1286 - 1298
Hauptverfasser:	Song, Yanjiao, Rui, Xiaoping, Li, Junjie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Atrous convolution (AC) attention mechanism Coders Convolution Data mining Feature extraction High resolution Image acquisition Image resolution Indexes joint loss function (JLF) Lake management Lakes Remote sensing Semantics Spatial discrimination Spatial resolution Sustainable development Urban areas urban water extraction Water bodies Water resources
ISSN:	1939-1404, 2151-1535
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Abstract	Accurate water extraction from urban remote sensing images holds great significance in assisting the formulation of river and lake management policies and ensuring the sustainable development of urban water resources. However, urban high-resolution remote sensing images encompass complex spatial and semantic information, which leads to disparities between the extracted water body features based on local and global information, consequently affecting the accuracy of urban water extraction. To tackle this issue, an attention-based encoder-decoder network was proposed. In this network, the backbone employing atrous convolution (AC) facilitated the acquisition of low-level and high-level features of urban remote sensing images at various scales. Integrated with the attention mechanism, the encoder-decoder structure extracted global features in both the spatial and channel domains. Subsequently, these two types of features were merged to yield the urban water segmentation. Moreover, considering both intersection over union and class weights, a joint loss function (JLF) was introduced to further enhance the accuracy of urban water extraction. Experimental results demonstrated the strong performance of the proposed method on both GID and LoveDA datasets.
AbstractList	Accurate water extraction from urban remote sensing images holds great significance in assisting the formulation of river and lake management policies and ensuring the sustainable development of urban water resources. However, urban high-resolution remote sensing images encompass complex spatial and semantic information, which leads to disparities between the extracted water body features based on local and global information, consequently affecting the accuracy of urban water extraction. To tackle this issue, an attention-based encoder–decoder network was proposed. In this network, the backbone employing atrous convolution (AC) facilitated the acquisition of low-level and high-level features of urban remote sensing images at various scales. Integrated with the attention mechanism, the encoder–decoder structure extracted global features in both the spatial and channel domains. Subsequently, these two types of features were merged to yield the urban water segmentation. Moreover, considering both intersection over union and class weights, a joint loss function (JLF) was introduced to further enhance the accuracy of urban water extraction. Experimental results demonstrated the strong performance of the proposed method on both GID and LoveDA datasets.
Author	Rui, Xiaoping Li, Junjie Song, Yanjiao
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Cites_doi	10.3390/rs11111351 10.1080/15481603.2023.2166396 10.1109/tgrs.2021.3131331 10.1007/978-3-030-01234-2_49 10.1007/978-3-030-01234-2_1 10.1080/17538947.2021.1995513 10.1109/CVPR.2017.660 10.1109/CVPR.2019.00154 10.1016/j.jag.2021.102497 10.1016/j.apacoust.2020.107714 10.1080/01431161.2022.2136505 10.1109/tgrs.2023.3276703 10.1109/CVPR.2019.00326 10.1080/15481603.2022.2142626 10.1109/CVPR.2016.90 10.1016/j.rse.2018.09.016 10.1109/ACCESS.2019.2908232 10.1109/ICCV.2019.00390 10.1016/j.rse.2019.111322 10.1117/1.JRS.15.042605 10.1109/jstars.2022.3215696 10.1109/CVPR.2018.00745 10.3390/w12071928 10.1109/CVPR.2019.00172 10.1016/j.jhydrol.2022.128202 10.1109/CVPR.2015.7298965 10.1016/j.isprsjprs.2022.08.019 10.1080/01431169608948714 10.1016/j.rse.2020.111706 10.3390/s19122769 10.1007/978-3-319-24574-4_28 10.1109/TPAMI.2017.2699184 10.3390/rs13163122 10.1109/CVPR.2018.00813 10.1109/CVPR.2018.00747 10.1080/17538947.2023.2166606 10.1109/JSTARS.2020.2971783 10.1109/cvpr.2018.00464 10.1109/JSTARS.2019.2929601 10.1109/jstars.2021.3104382 10.1016/j.jag.2022.103103 10.3390/ijgi9040189 10.3390/rs13101912 10.3390/s16071075 10.1109/tgrs.2022.3207551 10.1109/tgrs.2023.3243954 10.1038/nature20584 10.1109/jstars.2022.3185245 10.1016/j.rse.2013.08.029 10.1080/01431161.2017.1341667 10.3390/s150613763
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References	ref13 ref12 ref56 ref15 Xu (ref7) 2005; 9 ref14 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 ref51 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 Longfei (ref5) 2021; 33 ref49 ref8 ref9 ref4 ref3 ref6 ref40 ref35 ref34 ref37 ref36 ref31 ref33 ref32 ref2 ref1 ref39 ref38 Chen (ref30) 2017 ref24 ref23 ref26 Wang (ref50) 2021 ref25 ref20 ref22 ref21 Chen (ref28) 2015 ref27 ref29
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SubjectTerms	Accuracy Atrous convolution (AC) attention mechanism Coders Convolution Data mining Feature extraction High resolution Image acquisition Image resolution Indexes joint loss function (JLF) Lake management Lakes Remote sensing Semantics Spatial discrimination Spatial resolution Sustainable development Urban areas urban water extraction Water bodies Water resources
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Title	AEDNet: An Attention-Based Encoder-Decoder Network for Urban Water Extraction From High Spatial Resolution Remote Sensing Images
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