Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

Muhammad Ahmad, Asad Khan, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Ahmed Sohaib, O Nibouche

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)
65 Downloads (Pure)


Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements.
Original languageEnglish
Article number1136
Pages (from-to)1-19
Number of pages19
JournalRemote Sensing
Issue number9
Publication statusPublished (in print/issue) - 13 May 2019


  • Active learning
  • Fuzziness
  • Hyperspectral imaging
  • Soft threshold
  • Spectral angle mapper


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