Decision fusion for the classification of urban remote sensing images
Decision fusion for the classification of urban remote sensing images
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DOI：
10.1109/tgrs.2006.876708
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发表时间：
2006-10-01
影响因子：
8.2
通讯作者：
Benediktsson, Jon Atli
中科院分区：
文献类型：
--
作者：
Fauvel, Mathieu;Chanussot, Jocelyn;Benediktsson, Jon Atli
通过考虑多个分类器的融合，提供冗余或互补的结果，从城市地区的非常高分辨率的遥感图像的分类解决。所提出的融合方法分为两个步骤。在第一步骤中，数据分别由每个分类器处理，并且算法为所考虑的类的每个像素提供隶属度。然后，在第二步中，使用模糊决策规则来根据分类器的能力聚集由算法提供的结果。在本文中，一个一般的框架，结合信息从几个单独的分类器在多类分类。提出了它是基于两种准确度的定义。第一个是逐点测量，它估计每个像素的每个分类器提供的信息的可靠性。通过将分类器的输出建模为模糊集，这种逐点可靠性被定义为模糊集的不确定度。第二个度量估计每个分类器的全局精度。它是由用户先验定义的。最后，将结果与由这两个精度度量所统治的自适应模糊算子进行聚合。该方法进行了测试和验证与IKONOS图像从城市地区的两个分类器。与单独使用不同的分类器相比，该方法提高了分类结果。该方法还与其他几种模糊融合方案进行了比较。
The classification of very high resolution remote sensing images from urban areas is addressed by considering the fusion of multiple classifiers which provide redundant or complementary results. The proposed fusion approach is in two steps. In a first step, data are processed by each classifier separately, and the algorithms provide for each pixel membership degrees for the considered classes. Then, in a second step, a fuzzy decision rule is used to aggregate the results provided by the algorithms according to the classifiers' capabilities. In this paper, a general framework for combining information from several individual classifiers in multiclass classification is. proposed. It is based on the definition of two measures of accuracy. The first one is a pointwise measure which estimates for each pixel the reliability of the information provided by each classifier. By modeling the output of a classifier as a fuzzy set, this pointwise reliability is defined as the degree of uncertainty of the fuzzy set. The second measure estimates the global accuracy of each classifier. It is defined a priori by the, user. Finally, the results are aggregated with an adaptive fuzzy operator ruled by these two accuracy measures. The method is tested and validated with two classifiers on IKONOS images from urban areas. The proposed method improves the classification results when compared with the separate use of the different classifiers. The approach is also compared with several other fuzzy fusion schemes.