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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
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 deepdoi:10.1109/jstars.2021.3122461 fatcat:mhyxzctzwve2ndpavi35526wwq