Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images
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Otros documentos de la autoría: Pla, Filiberto; Parajuli, Janak; Fernandez-Beltran, Ruben; kang, jian
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comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Título
Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 ImagesFecha de publicación
2022-08-15Editor
IEEECita bibliográfica
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.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/document/9855876Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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. [-]
Publicado en
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 15)Datos relacionados
https://github.com/rufernan/ADCNNEntidad financiadora
Ministerio de Ciencia e Innovación | National Natural Science Foundation of China
Código del proyecto o subvención
PID2021-128794OB-I00 | 62101371
Derechos de acceso
info:eu-repo/semantics/openAccess
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