Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
comunitat-uji-handle4:
INVESTIGACIONMetadata
Title
Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 ImagesDate
2022-08-15Publisher
IEEEBibliographic citation
Parajuli, J., Fernandez-Beltran, R., Kang, J., & Pla, F. (2022). 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, 15, 6804-6816.Type
info:eu-repo/semantics/articlePublisher version
https://ieeexplore.ieee.org/document/9855876Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
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 ... [+]
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 Sentinel2 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. [-]
Is part of
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 15)Related data
https://github.com/rufernan/ADCNNFunder Name
Ministerio de Ciencia e Innovación | National Natural Science Foundation of China
Project code
PID2021-128794OB-I00 | 62101371
Rights
info:eu-repo/semantics/openAccess
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