Shadow Detection and classification using image processing: A Review

Very-High-Resolution (VHR) satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. The proposed system reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the art shadow detection, and segmentation algorithms using kmeans and SVM algorithm. In the first approach, we first segment the images into multiple parts using RGB segments and at the same time perform clustering using kmeans algorithm. After segmentation we use SVM algorithm for classification of shadow from VHR Images.