Mapping Dongting Lake Wetland Utilizing Time Series Similarity, Statistical Texture, and Superpixels With Sentinel-1 SAR Data

Wetland is of significant ecological value, which is very important and challenging for large-scale mapping. Sentinel-1 can continuously record wetland changes with its all-day, all-weather working capability. How to fully utilize multidimensional information, such as time and statistical texture of synthetic aperture radar (SAR) image, to classify wetlands more accurately has become a research focus. Thus, this article constructed time series similarity parameters to describe the temporal change information of targets in wetland and introduced G
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statistical texture parameters to describe texture characteristics of SAR images. Combining multifeatures and superpixels, we proposed a classification method based on random forest (RF) to map Dongting Lake wetland. The overall classification accuracy was 97.57%, and the Kappa coefficient was 0.97. The classification accuracy of reed beach and grass marsh was above 95%. The results showed the proposed SAR time series similarity features could effectively utilize dynamical information among classes and was helpful to identify mudflat, grass marsh, and reed beach with high dynamics in wetland. The introduced statistical texture features expressed the heterogeneity of targets, and enhanced the recognition and extraction ability of forest beach, mudflat, and reed beach. Compared with support vector machine, decision tree, and RF classification methods, RF with superpixels optimized not only got high precision but also could effectively reduce the pepper-salt error in classification, because of the consideration of superpixels context information.