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LCS-EnsemNet: A Semisupervised Deep Neural Network for SAR Image Change Detection With Dual Feature Extraction and Label-Consistent Self-Ensemble
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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 each image pixel; and adequate labeled samples are quite laborious and timeconsuming to collect, which is the major limitation for supervised methods. In this article, we develop a novel deep
doi:10.1109/jstars.2021.3122461
fatcat:mhyxzctzwve2ndpavi35526wwq