Mapping shadows in very high-resolution satellite data using HSV and edge detection techniques

Multispectral scanners (MSS) such as IKONOS have very high spatial resolution and therefore, provide excellent source of information about terrestrial features. The images from these scanners may contain shadows that can lead to partial or complete loss of radiometric information, leading to misinterpretation or inaccurate classification. In addition, the identification of shadows is critical for several applications. The goal of this study is to develop computer-based algorithms to detect shadows from IKONOS panchromatic (1 × 1 m) and MSS bands (4 × 4 m). We converted subsets of IKONOS pan and MSS images over New York City to HSV color space, and used histogram analysis to determine an intensity threshold. Potential sunlit and shadow areas were demarcated and edge detection techniques were employed to eliminate the non-shadow, low-intensity areas and identify shadow areas on the image subsets. We tested the results on a time series of datasets to develop a robust model that has the capability to detect shadows and extract them from high-resolution satellite imagery.