@Article{OldoniSancPico:2022:AcAsCl,
               author = "Oldoni, Lucas Volochen and Sanches, Ieda Del'Arco and Picoli, 
                         Michelle Cristina Ara{\'u}jo",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Accuracy assessment of cloud mask detection algorithms for cbers-4 
                         wfi imagery",
              journal = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial 
                         Information Sciences",
                 year = "2022",
               volume = "3",
                pages = "61--67",
             keywords = "ATSA, CMASK, cloud shadow, cloud mask, Wide-Field Imager.",
             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.",
                  doi = "10.5194/isprs-annals-V-3-2022-61-2022",
                  url = "http://dx.doi.org/10.5194/isprs-annals-V-3-2022-61-2022",
                 issn = "0924-2716",
                label = "lattes: 2456184661855977 2 OldoniSancPico:2022:ACASCL",
             language = "en",
           targetfile = "isprs-annals-V-3-2022-61-2022.pdf",
        urlaccessdate = "04 maio 2024"
}