SVM-Based Cloud Detection Using Combined Texture Features

Abstract

Cloud detection of satellite images is a challenging task. Extracting discriminative image features is one of the crucial steps for accurate cloud detection. In this chapter, we introduce a texture-based image feature that combines the merits of grey-level co-occurrence matrix (GLCM) features and rotation invariant uniform local binary pattern (RIULBP). The cloud detection method based on proposed feature consists of three steps: (1) Enhancing the image and dividing it into non-overlap patches; (2) Calculating GLCM features and RIULBP independently on patches and determining their optimal key parameters based on cloud detection performance; (3) Combining optimal GLCM features and RIULBP and feeding them into SVM classifier to identify patches with cloud. The proposed detection method is quantitatively compared to methods that only use GLCM features and RIULBP. The overall detection accuracy shows that our proposed method outperforms the GLCM and RIULBP method on real images. The proposed cloud detection method facilitates cloud segmentation and classification tasks, which can aid to better analysis of satellite image.