A Coarse-to-Fine Method for Cloud Detection in Remote Sensing Images

Abstract

In this letter, a coarse-to-fine unsupervised method is proposed for cloud detection in remote sensing images. First, the color, texture, and statistical features of the remote sensing images are extracted with the color transform, dark channel estimation, Gabor filtering, and local statistical analysis methods. Then, an initial cloud detection map can be obtained by performing the support vector machines (SVM) on the stacked features, in which the SVM is trained with a set of samples automatically labeled by processing the dark channel of the original image with several thresholding and morphological operations. Finally, guided filtering is used to refine the boundaries in the initial detection map, which further improves the cloud detection accuracy. Experiments performed on several real remote sensing images demonstrate that the proposed method show better detection performances with respect to several recently proposed cloud detection methods in terms of both quantitative and visual comparisons.