DEVELOPMENT OF CLOUD DETECTION METHOD FOR CAS500-1 IMAGERY

Abstract

Clouds are typically characterized by high reflectance and low brightness temperature. They are generally classified as noise in optical land monitoring satellites. In particular, the presence of clouds has a decisive effect on the quality of follow-up data, so they must be detected. In this study, pre-processing was applied to effectively detect clouds while minimizing noise using CAS500-1 images with visible and near-infrared bands. First, the RGB color space is converted to the HSV color space. Next, a triangle thresholding method is applied to the value channel, which exhibits the highest correlation with pixel brightness, to extract bright objects. Then, the maximum likelihood method is applied to differentiate between bright objects and cloud candidate objects. Finally, threshold values for cloud detection are automatically determined to create initial cloud maps using the statistical values derived from the cloud candidate objects. We compared the results generated by the single thresholding method to verify the performance of the proposed method. As a result, the proposed method was able to detect clouds more accurately by considering the reflectance characteristics of each image. Moreover, except for cloud objects, the rest of the bright objects (white roofs, concrete roads, sand, etc.) were minimized. Our experiments showed high stability despite the absence of shortwave infrared and thermal infrared bands, which are effective for cloud detection.