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@Article{ReisDutrSantEsca:2017:ExMuCh,
               author = "Reis, Mariane Sousa and Dutra, Luciano Vieira and Sant'Anna, 
                         Sidnei Jo{\~a}o Siqueira and Escada, Maria Isabel Sobral",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Examining multi-legend change detection in amazon with pixel and 
                         region based methods",
              journal = "Remote Sensing",
                 year = "2017",
               volume = "9",
               number = "1",
             keywords = "Amazon, Change detection, Multi-legend, Pixel based 
                         classification, Region based classification.",
             abstract = "Post-classification comparison is one of the most widely used 
                         change detection methods. However, it presents several operational 
                         problems that are often ignored, such as the occurrence of 
                         impossible transitions, difficulties in accuracy assessment and 
                         results not accurate enough for the purpose. This work aims to 
                         evaluate post-classification comparison change detection results 
                         obtained from LANDSAT5/TM data in a region of the Brazilian 
                         Amazon, using three legends in different levels of detail and both 
                         pixel wise and region based classifiers. A distinctive 
                         characteristic of the used approach is that each change mapping is 
                         the result of the combination of 100 land cover classifications 
                         for each date, obtained using varied training samples. This 
                         approach allowed to account for the training samples choice into 
                         the methodology, as well as the construction of confidence 
                         mappings. We presented and discussed different approaches for 
                         evaluating change results, such as the likelihood of land cover 
                         transitions occurring within the study area and time gap, the use 
                         of rectangular matrices to incorporate the occurrence of 
                         impossible or non evaluable changes and classification 
                         uncertainty. In general, change mappings obtained from region 
                         based classifications showed better results than the ones obtained 
                         from pixel based classifications. Globally, the use of region 
                         based approaches, in contrast to pixel based ones, led to an 
                         increase in accuracy of 15.5% for the change mapping from the most 
                         detailed legend, 7.8% for the one with the legend with 
                         intermediate level of detail and 3.6% for the less detailed one. 
                         In addition, individual transitions between land cover classes 
                         were better identified using region based approaches, with the 
                         exception of transitions from a non agriculture class to an 
                         agricultural one. The proposed quality mappings are useful to help 
                         to evaluate the change mappings, mainly in legend levels with 
                         higher level of detail and if reference samples are unreliable or 
                         unavailable. It was possible to access, in a spatially explicit 
                         way, that at least 29.0% of the pixel based change mapping and 
                         21.9% of the region based one from the most detailed legend were 
                         erroneous classified, without ground truth information on the 
                         evaluated date. These values decreased to 0.5% and 1.4% 
                         (respectively the pixel and region based approaches) for results 
                         with the legend with the intermediate level of detail and are non 
                         existent in the results from the less detailed legend. The more 
                         generalized the legend (lower number of classes), the most similar 
                         are the accuracy of region and pixel based change mappings. These 
                         accuracy values also increase as fewer classes are considered in 
                         the legend, since similar classes are assembled during clustering, 
                         which reduces the overlap between groups. However, this accuracy 
                         is still low for operational purposes in areas with few changes, 
                         even considering the very high accuracy of the land cover 
                         classifications used to generate the change mappings (land cover 
                         classification with Overall Accuracy higher than 0.98 resulted in 
                         change mappings with Overall Accuracy around 0.83).",
                  doi = "10.3390/rs9010077",
                  url = "http://dx.doi.org/10.3390/rs9010077",
                 issn = "2072-4292",
             language = "en",
           targetfile = "reis_examining.pdf",
        urlaccessdate = "25 nov. 2020"
}


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