Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images
被引:16
作者:
Parajuli, Janak
论文数: 0引用数: 0
h-index: 0
机构:
Univ Jaume 1, Inst New Imaging Technol, Dept Comp Languages & Syst, E-12071 Castellon De La Plana, SpainUniv Jaume 1, Inst New Imaging Technol, Dept Comp Languages & Syst, E-12071 Castellon De La Plana, Spain
Parajuli, Janak
[1
]
论文数: 引用数:
h-index:
机构:
Fernandez-Beltran, Ruben
[2
]
Kang, Jian
论文数: 0引用数: 0
h-index: 0
机构:
Soochow Univ, Sch Elect & Informat Engn, Dept Elect Sci & Technol, Suzhou 215006, Peoples R ChinaUniv Jaume 1, Inst New Imaging Technol, Dept Comp Languages & Syst, E-12071 Castellon De La Plana, Spain
Kang, Jian
[3
]
Pla, Filiberto
论文数: 0引用数: 0
h-index: 0
机构:
Univ Jaume 1, Inst New Imaging Technol, Dept Comp Languages & Syst, E-12071 Castellon De La Plana, SpainUniv Jaume 1, Inst New Imaging Technol, Dept Comp Languages & Syst, E-12071 Castellon De La Plana, Spain
Pla, Filiberto
[1
]
机构:
[1] Univ Jaume 1, Inst New Imaging Technol, Dept Comp Languages & Syst, E-12071 Castellon De La Plana, Spain
Feature extraction;
Water resources;
Remote sensing;
Earth;
Indexes;
Data mining;
Water conservation;
Convolutional neural networks (CNNs);
dense networks;
residual attention networks;
Sentinel-2;
water bodies;
INDEX;
NDWI;
D O I:
10.1109/JSTARS.2022.3198497
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
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.
机构:
USA, ERDC, Fluorescence Spect Lab, Alexandria, VA 22315 USA
Virginia Commonwealth Univ, Dept Biol, Richmond, VA 23284 USAIISTA Univ Cordoba, Fluvial Dynam & Hydrol Res Grp, Cordoba 14071, Spain
机构:
European Commiss, Joint Res Ctr, Inst Environm & Sustainabil, Land Resource Management Unit, I-21027 Ispra, Varese, ItalyEuropean Commiss, Joint Res Ctr, Inst Environm & Sustainabil, Land Resource Management Unit, I-21027 Ispra, Varese, Italy
Belward, Alan S.
;
Skoien, Jon O.
论文数: 0引用数: 0
h-index: 0
机构:
European Commiss, Joint Res Ctr, Inst Environm & Sustainabil, Land Resource Management Unit, I-21027 Ispra, Varese, ItalyEuropean Commiss, Joint Res Ctr, Inst Environm & Sustainabil, Land Resource Management Unit, I-21027 Ispra, Varese, Italy
机构:
Liaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China
Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R ChinaLiaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China
Chen, Yang
;
Fan, Rongshuang
论文数: 0引用数: 0
h-index: 0
机构:
Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R ChinaLiaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China
Fan, Rongshuang
;
Yang, Xiucheng
论文数: 0引用数: 0
h-index: 0
机构:
Univ Strasbourg, ICube Lab, F-67000 Strasbourg, FranceLiaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China
Yang, Xiucheng
;
Wang, Jingxue
论文数: 0引用数: 0
h-index: 0
机构:
Liaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R ChinaLiaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China
机构:
USA, ERDC, Fluorescence Spect Lab, Alexandria, VA 22315 USA
Virginia Commonwealth Univ, Dept Biol, Richmond, VA 23284 USAIISTA Univ Cordoba, Fluvial Dynam & Hydrol Res Grp, Cordoba 14071, Spain
机构:
European Commiss, Joint Res Ctr, Inst Environm & Sustainabil, Land Resource Management Unit, I-21027 Ispra, Varese, ItalyEuropean Commiss, Joint Res Ctr, Inst Environm & Sustainabil, Land Resource Management Unit, I-21027 Ispra, Varese, Italy
Belward, Alan S.
;
Skoien, Jon O.
论文数: 0引用数: 0
h-index: 0
机构:
European Commiss, Joint Res Ctr, Inst Environm & Sustainabil, Land Resource Management Unit, I-21027 Ispra, Varese, ItalyEuropean Commiss, Joint Res Ctr, Inst Environm & Sustainabil, Land Resource Management Unit, I-21027 Ispra, Varese, Italy
机构:
Liaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China
Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R ChinaLiaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China
Chen, Yang
;
Fan, Rongshuang
论文数: 0引用数: 0
h-index: 0
机构:
Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R ChinaLiaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China
Fan, Rongshuang
;
Yang, Xiucheng
论文数: 0引用数: 0
h-index: 0
机构:
Univ Strasbourg, ICube Lab, F-67000 Strasbourg, FranceLiaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China
Yang, Xiucheng
;
Wang, Jingxue
论文数: 0引用数: 0
h-index: 0
机构:
Liaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R ChinaLiaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China