LCS-EnsemNet: A Semisupervised Deep Neural Network for SAR Image Change Detection With Dual Feature Extraction and Label-Consistent Self-Ensemble

LCS-EnsemNet: A Semisupervised Deep Neural Network for SAR Image Change Detection With Dual Feature Extraction and Label-Consistent Self-Ensemble

Change detection (CD) in synthetic aperture radar (SAR) images faces two challenging problems limiting the detection performance: inherent speckle noise in SAR data causes the overlapping nature of changed and unchanged classes and, thus, affects the image understanding for inferring category of eac...

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Detalles Bibliográficos
Autores principales: Jian Wang, Yinghua Wang, Bo Chen, Hongwei Liu
Formato: article
Lenguaje:EN
Publicado: IEEE 2021
Materias:
Change detection (CD)
deep neural network (DNN)
label-consistent self-ensemble
semisupervised learning (SSL)
spatially enhanced (SE) difference image (DI)
synthetic aperture radar (SAR)
Ocean engineering
TC1501-1800
Geophysics. Cosmic physics
QC801-809
Acceso en línea:https://doaj.org/article/2fa5a4f1708f43a4a1ddec9e489c119e
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https://doaj.org/article/2fa5a4f1708f43a4a1ddec9e489c119e

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