A Practical Plateau Lake Extraction Algorithm Combining Novel Statistical Features and Kullback–Leibler Distance Using Synthetic Aperture Radar Imagery

Due to wind-induced waves, dry sand, wet snow, and terrain shadows, the lake extraction from synthetic aperture radar (SAR) imagery in the Qinghai-Tibet plateau is accompanied by false alarms. In this article, a practical plateau lake extraction algorithm combining novel statistical features and Kullback–Leibler distance (KLD) using SAR imagery has been proposed. First, a mathematical description for the plateau lake surface called object-based generalized gamma distribution (OGΓD) features has been proposed, which is able to suppress the false alarms by using spatial context information as the large-scale descriptor. Second, the random forest classifier is used to train a multifeature set, including conventional texture features and OGΓD features, and output an initial labeling result. Finally, to suppress the false alarms in the initial lake extraction results, automatic postprocessing based on KLD has been used. The algorithm is tested by several experiments using Sentinel-1 SAR data, performing better than the state-of-the-art algorithms, achieving the overall accuracy of 99.54% while maintaining a false-alarm rate of 0.32%.