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Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series
2020
Remote Sensing
High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the
doi:10.3390/rs12030478
fatcat:eyyvhancajdpddqteo5kejqmgi