AUTOMATIC SEGMENTATION OF CLOUD OBJECTS ON THE HIGH SPATIAL RESOLUTION SURFACE IMAGE
This paper describes novel clouds segmentation algorithm, designed to process high spatial resolution EO imagery, obtained by equipment that uses principle of light flux spatial separation to different spectral channels sensors. Causes, that makes hard to apply wide used cloud detection algorithms in this situation are reviewed. Shown, that multistage algorithm, that uses colometric algorithm for preliminary clouds detection, Bayes classifier for more accurate detection and stereophotogrammetric filtration of results, have good speed and reliability. Shown, that using of spereophotogrammetric filtering of preprocessed data significantly improves segmentation quality, while speed decrease still small. Shown examples, that illustrates ability of developed algorithm to distinguish clouds from snow, desert and infrastructure objects. Given a comparison between developed algorithm and wide used algorithms and shown superiority of new algorithm. Performance and quality estimates are provided.