Research on superpixels segmentation of cloud remote sensing images based on density features

Abstract

Remote sensing images are widely used in earth observation. However, the existence of clouds seriously affects the interpretation of remote sensing images. In order to improve the accuracy of cloud detection, it’s usually necessary to complete the segmentation of the cloud boundary before cloud detection. Based on the Simple Linear Iterative Clustering (SLIC) algorithm, an improved SLIC superpixels segmentation method based on density feature is proposed to realize the segmentation of cloud remote sensing images. First, to generate the initial clustering center , the density peak clustering method is used instead of the uniform setting method. Then, in the calculation of distance measurement, we added the local density distance term. Finally, we get the superpixels segmented image by iteration. Four remote sensing images with different underlying surfaces were selected as the test data. The comparison experiments with other two algorithms show that the algorithm promoted in this paper shows superior performance in boundary recall (BR) rate and the error rate is lower than other algorithms in cloud detection.