LAUSR

As one of the most important steps in classification and mapping research, sample acquisition and updating cost a lot of time and energy of researchers. The high temporal and spatial dynamic characteristics of wetland make the reliable wetland sample selection more challenging. It is especially important for time series wetland classification to address the problem of how to apply sample sets to images with different periods. This article evaluated the reliability of the historical samples by using optical and synthetic aperture radar remote sensing data from the perspective of water inundation frequency, and three sample migration methods based on rule set, reclassification, and spectral similarity were proposed to carry out wetland classification experiment in the Tibetan Plateau. In addition, the relationship between the number of samples and classification accuracy is analyzed. The migration and reuse of sample sets can quickly obtain accurate wetland sample sets, which lays the foundation for the study of wetland mapping in time series.