Blurred Labeling Segmentation Algorithm for Hyperspectral Images

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9330)


This work is focusing on the hyperspectral imaging classification, which is nowadays a focus of intense research. The hyperspectral imaging is widely used in agriculture, mineralogy, or food processing to enumerate only a few important domains. The main problem of such image classification is access to the ground truth, because it needs the experienced experts. This work proposed a novel three-stage image segmentation method, which prepares the data for the classification and employs the active learning paradigm which reduces the expert works on image. The proposed approach was evaluated on the basis of the computer experiments carried out on the benchmark hyperspectral datasets.


Machine learning Hyperspectral imaging Image processing Classification Classifier ensemble 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland
  2. 2.University of the Basque CountryLeioaSpain
  3. 3.ENGINE CenterWroclaw University of TechnologyWroclawPoland

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