Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images
Monitoring water bodies from remote sensing data is certainly an essential task to supervise the actual conditions of the available water resources for environment conservation, sustainable development, and many other applications. Being Sentinel-2 images some of the most attractive data, existing t...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9855876/ |
| _version_ | 1811281272676286464 |
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| author | Janak Parajuli Ruben Fernandez-Beltran Jian Kang Filiberto Pla |
| author_facet | Janak Parajuli Ruben Fernandez-Beltran Jian Kang Filiberto Pla |
| author_sort | Janak Parajuli |
| collection | DOAJ |
| description | Monitoring water bodies from remote sensing data is certainly an essential task to supervise the actual conditions of the available water resources for environment conservation, sustainable development, and many other applications. Being Sentinel-2 images some of the most attractive data, existing traditional index-based and deep learning-based water extraction methods still have important limitations in effectively dealing with large heterogeneous areas since many types of water bodies with different spatial-spectral complexities are logically expected. Note that, in this scenario, optimal feature abstraction and neighborhood information may certainly vary from water to water pixel, however existing methods are generally constrained by a fix abstraction level and amount of land cover context. To address these issues, this article presents a new attentional dense convolutional neural network (AD-CNN) especially designed for water body extraction from Sentinel-2 imagery. On the one hand, the AD-CNN exploits dense connections to allow uncovering deeper features while simultaneously characterizing multiple data complexities. On the other hand, the proposed model also implements a new residual attention module to dynamically put the focus on the most relevant spatial-spectral features for classifying water pixels. To test the performance of the AD-CNN, a new water database of Nepal (WaterPAL) is also built. The conducted experiments reveal the competitive performance of the proposed architecture with respect to several traditional index-based and state-of-the-art deep learning-based water extraction models. |
| first_indexed | 2024-04-13T01:29:36Z |
| format | Article |
| id | doaj.art-2b071debe6a7498cb43729f8287d7900 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-13T01:29:36Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-2b071debe6a7498cb43729f8287d79002022-12-22T03:08:32ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01156804681610.1109/JSTARS.2022.31984979855876Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 ImagesJanak Parajuli0Ruben Fernandez-Beltran1https://orcid.org/0000-0003-1374-8416Jian Kang2https://orcid.org/0000-0001-6284-3044Filiberto Pla3https://orcid.org/0000-0003-0054-3489Department of Computer Languages and Systems, Institute of New Imaging Technologies, University Jaume I, Castellón de la Plana, SpainDepartment of Computer Science and Systems, University of Murcia, Murcia, SpainSchool of Electronic and Information Engineering Department of Electronic Science and Technology, Soochow University, Suzhou, ChinaDepartment of Computer Languages and Systems, Institute of New Imaging Technologies, University Jaume I, Castellón de la Plana, SpainMonitoring water bodies from remote sensing data is certainly an essential task to supervise the actual conditions of the available water resources for environment conservation, sustainable development, and many other applications. Being Sentinel-2 images some of the most attractive data, existing traditional index-based and deep learning-based water extraction methods still have important limitations in effectively dealing with large heterogeneous areas since many types of water bodies with different spatial-spectral complexities are logically expected. Note that, in this scenario, optimal feature abstraction and neighborhood information may certainly vary from water to water pixel, however existing methods are generally constrained by a fix abstraction level and amount of land cover context. To address these issues, this article presents a new attentional dense convolutional neural network (AD-CNN) especially designed for water body extraction from Sentinel-2 imagery. On the one hand, the AD-CNN exploits dense connections to allow uncovering deeper features while simultaneously characterizing multiple data complexities. On the other hand, the proposed model also implements a new residual attention module to dynamically put the focus on the most relevant spatial-spectral features for classifying water pixels. To test the performance of the AD-CNN, a new water database of Nepal (WaterPAL) is also built. The conducted experiments reveal the competitive performance of the proposed architecture with respect to several traditional index-based and state-of-the-art deep learning-based water extraction models.https://ieeexplore.ieee.org/document/9855876/Convolutional neural networks (CNNs)dense networksresidual attention networksSentinel-2water bodies |
| spellingShingle | Janak Parajuli Ruben Fernandez-Beltran Jian Kang Filiberto Pla Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Convolutional neural networks (CNNs) dense networks residual attention networks Sentinel-2 water bodies |
| title | Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images |
| title_full | Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images |
| title_fullStr | Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images |
| title_full_unstemmed | Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images |
| title_short | Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images |
| title_sort | attentional dense convolutional neural network for water body extraction from sentinel 2 images |
| topic | Convolutional neural networks (CNNs) dense networks residual attention networks Sentinel-2 water bodies |
| url | https://ieeexplore.ieee.org/document/9855876/ |
| work_keys_str_mv | AT janakparajuli attentionaldenseconvolutionalneuralnetworkforwaterbodyextractionfromsentinel2images AT rubenfernandezbeltran attentionaldenseconvolutionalneuralnetworkforwaterbodyextractionfromsentinel2images AT jiankang attentionaldenseconvolutionalneuralnetworkforwaterbodyextractionfromsentinel2images AT filibertopla attentionaldenseconvolutionalneuralnetworkforwaterbodyextractionfromsentinel2images |