Cloud detection in East Asian urban areas in VIIRS day/night band by texture analysis

Abstract

In this study, we investigated the effect of clouds on night light using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) in East Asian urban areas. Focusing on the Mie scattering phenomenon that occurs when night light passes through thin clouds, we investigated the effects of clouds using the Gray Level Co-occurrence Matrix (GLCM) features of texture analysis.

To reduce the influence of non-economic activity areas at night, we proposed GLCM features with the background area treated to 0. To verify the effectiveness of the proposed features, we compared the accuracy of cloud classification by machine learning. As a result, the accuracy of GLCM features with 0-processed background area improved by 3 to 5% in Support Vector Classification (SVC) and 0.5 to 2% in Random Forest classifier (RFC). It was found that GLCM contrast and ND (co, ho) are effective features for RFC. ND is a normalized index of contrast and dissimilarity. We also found that the optimal ROI size for GLCM is 33×33 pixels. Finally, as a result of comparing the cloud mask and the RFC results, it was found that the method of this study is effective.