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An important aspect of research in the remote sensing field is to objectively compare different classifiers. This is the foundation of hundreds of research projects and in this paper we will address some raising concerns when evaluating solutions for classification of data sets with skewed class distributions. The quality of assessment is based on the problem specified by the user and the corresponding...
We present a method based on Markov Random Fields (MRFs) for conducting decision level fusion of segments derived from multiple images of the same region. These images are not required to share the same resolution or sensor characteristics. By working at the segment level we preserve the advantages of segment based image classification while also incorporating the benefits of using multiple image...
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