SEGMENTATION BASED CLOUD AND CLOUD SHADOW DETECTION IN SATELLITE IMAGERY

Abstract

One of the main source of noises in remote sensing satellite imagery is regional clouds and shadows of these clouds caused by atmospheric conditions. In many studies, these clouds and shadows are masked with multi-temporal imagery taken from the same area to decrease effects of misclassification and deficiency in different image processing techniques, such as change detection and NDVI (Normalized Difference Vegetation Index). This problem is surpassed in many studies by mosaicking with different images obtained from different acquisition dates of the same region. The main step of all studies that cover cloud cloning or cloud detection is the detection of clouds from a satellite image. In this study, clouds and shadow patches are classified by using a spectral feature based rule set created after segmentation process of Landsat 8 image. Not only spectral characteristics but also structural parameters like pattern, area and dimension are used to detect clouds and shadows. Rule set of classification is developed within a transferable approach to reach a scene independent method. Results are tested with different satellite imageries from different areas to test transferability and compared with other state-of art methods in the literature.

Keywords

• Multi-temporal satellite imagery
• cloud detection
• cloud cloning
• segmentation
• threshold
• ruleset