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The use of land cover change likelihood for improving land cover classification

机译：利用土地覆被变化可能性改善土地覆被分类

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The likelihood of transitions between pairs of land cover and land use classes in a given time interval and environmental context can be used to impose classification restrictions on an image or to evaluate results. This study presents a methodology for using the likelihood of transitions between classes to improve land cover classification, given a base map (a supposedly accurate map for the same area in another date) and a set of previously classified images. These improved land cover classified images were named conditioned classified images. We aimed to classify one Synthetic Aperture Radar image and an optical one, both from June 2010, using two land cover legends in different level of detail for a region in the Brazilian Amazon. We used both a classified image from 2008 (also in two legends levels) and the data from the Programme for the Estimation of Deforestation in Brazilian Amazon (PRODES) from 2008 as base maps, and presented the likelihood of transitions between the considered classes. The proposed methodology resulted in conditioned classified images with higher Overall Accuracy than the one that does not consider the base maps and the likelihood of transitions. The conditioned classified images presented unlabeled areas due to classification errors in the input data. It is important to highlight that these areas are probably misclassified in maps obtained without using likelihood transition and base maps, since they are impossible to occur in the field.
机译：在给定的时间间隔和环境环境中，成对的土地覆盖和土地利用类别之间转换的可能性可用于对图像施加分类限制或评估结果。这项研究提出了一种利用类别之间转换的可能性来改善土地覆被分类的方法，给出了基本地图（在另一个日期中同一区域的推测准确地图）和一组先前分类的图像。这些改进的土地覆盖分类图像称为条件分类图像。我们的目标是从2010年6月开始，对两个合成孔径雷达图像和一个光学图像进行分类，分别使用巴西亚马逊地区某个区域的两个土地覆盖图例进行不同程度的详细说明。我们使用了2008年的分类图像（也处于两个图例级别）和2008年巴西亚马逊森林砍伐评估计划（PRODES）的数据作为底图，并提出了所考虑类别之间转换的可能性。所提出的方法所产生的条件分类图像比不考虑底图和过渡可能性的分类图像具有更高的整体精度。由于输入数据中的分类错误，调节后的分类图像呈现未标记区域。重要的是要强调，这些区域可能在不使用似然转换和基础图的情况下获得的地图中被错误分类，因为它们不可能在野外发生。

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