Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images
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Títol
Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 ImagesData de publicació
2022-08-15Editor
IEEECita bibliogràfica
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.Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://ieeexplore.ieee.org/document/9855876Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
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. [-]
Publicat a
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 15)Dades relacionades
https://github.com/rufernan/ADCNNEntitat finançadora
Ministerio de Ciencia e Innovación | National Natural Science Foundation of China
Codi del projecte o subvenció
PID2021-128794OB-I00 | 62101371
Drets d'accés
info:eu-repo/semantics/openAccess
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