ACCURACY ASSESSMENT OF CLOUD MASK DETECTION ALGORITHMS FOR CBERS-4 WFI IMAGERY

Abstract

Clouds limit the potential use of optical images. Proper clouds and cloud shadows detection are necessary steps for optical image applications. Few algorithms are flexible in detecting clouds in images with a limited number of bands, such as the Wide-Field Imager (WFI) sensor on board the China-Brazil Earth Resources Satellite (CBERS-4), which has four spectral bands (blue, green, red, and near-infrared). Therefore, this work aims to assess the accuracy of two cloud detection algorithms: CMASK and ATSA, and evaluate the influence of the ATSA configuration parameters. We selected four regions in Brazil for our analysis. In all cases, ATSA presented overall accuracy (OA) superior to CMASK. While the ATSA obtained OA greater than 0.91 for all analyzes, the OA from CMASK did not exceed 0.84. CMASK presented commission errors for the No Clear class (combination of Cloud and Cloud shadow) and inclusion errors for the Clear class close to zero. However, many errors of omission of clouds misclassified as the Clear class was observed. The ATSA algorithm presented a better balance between inclusion errors and omission errors. Our results can be used as guidance for choosing a cloud mask algorithm for the CBERS-4 WFI images and for analysis considering the images from WFI on board CBERS-4A and Amazonia-1, as they have similar characteristics.