@InProceedings{ZorteaSalbTrie:2012:ObClCl,
               author = "Zortea, Maciel and Salberg, Arnt-Børre and Trier, Øivind Due",
                title = "Object-based cloud and cloud shadow detection in Landsat images 
                         for tropical forest monitoring",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Feitosa, Raul Queiroz and Costa, Gilson Alexandre Ostwald Pedro da 
                         and Almeida, Cl{\'a}udia Maria de and Fonseca, Leila Maria Garcia 
                         and Kux, Hermann Johann Heinrich",
                pages = "326--331",
         organization = "International Conference on Geographic Object-Based Image 
                         Analysis, 4. (GEOBIA).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Cloud detection, shadows, classification, segmentation, Landsat.",
             abstract = "Clouds and cloud shadows often obscure parts of images acquired by 
                         optical space-borne sensors. The clouds and cloud shadows need to 
                         be detected and labeled as missing data. This enables subsequent 
                         methods to make their own decisions about how the missing data 
                         should be handled. Here we propose an automatic method to detect 
                         daytime cloud and cloud shadows in the context of tropical forest 
                         monitoring. In particular, we focus on Landsat 5 TM and Landsat 7 
                         ETM+ images. In addition to the original bands, we investigate the 
                         use of additional spectral-derived features, based on pixel-wise 
                         differences, ratios, and maximum values derived for all 
                         combinations of pairs of top-of-the atmosphere reflectance bands. 
                         The subset of features retained for classification, and the 
                         boundaries of the classes in the feature space, were identified by 
                         optimizing the accuracy of the proposed method using samples 
                         collected from spatially disjoint scenes, acquired in different 
                         time periods, in an attempt to increase the generalization 
                         capability of the proposed approach when applied to unseen scenes. 
                         When a new image is to be classified, the idea is to first segment 
                         it locally using the Statistical Region Merging algorithm (Nock 
                         and Nielsen, 2004). Cloud and cloud shadow masks are then obtained 
                         by classifying the averaged pixel values, inside each segment, 
                         instead of individual pixels. Finally a simple cloud shape 
                         matching algorithm is used to reduce false detection of cloud 
                         shadow areas. We found that the proposed object-based technique 
                         reduces the spatial noise of the final classified map when 
                         compared to traditional single pixel classification. The accuracy 
                         of the proposed method appears to be comparable to two alternative 
                         algorithms selected for benchmark purposes.",
  conference-location = "Rio de Janeiro",
      conference-year = "May 7-9, 2012",
                 isbn = "978-85-17-00059-1",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP8W/3BT7ED5",
                  url = "http://urlib.net/ibi/8JMKD3MGP8W/3BT7ED5",
           targetfile = "094.pdf",
                 type = "Forest Analysis",
        urlaccessdate = "04 maio 2024"
}