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Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

1
Dipartimento di Matematica e Informatica—MIFT, University of Messina, Messina 98121, Italy
2
Department of Computer Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan
3
Institute of Data Science and Artificial Intelligence, Innopolis University, Innopolis 420500, Russia
4
School of Computer Science, South China Normal University, Guangzhou 510000, China
5
Institute of Software Development and Engineering, Innopolis University, Innopolis 420500, Russia
6
Faculty of Computing, Engineering and the Built Environment, Ulster University, Newtownabbey, Co Antrim BT37 0QB, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2019, 11(9), 1136; https://doi.org/10.3390/rs11091136
Received: 26 March 2019 / Revised: 28 April 2019 / Accepted: 5 May 2019 / Published: 13 May 2019
(This article belongs to the Special Issue Image Optimization in Remote Sensing)
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Abstract

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. View Full-Text
Keywords: hyperspectral imaging; active learning; fuzziness; spectral angle mapper; soft threshold hyperspectral imaging; active learning; fuzziness; spectral angle mapper; soft threshold
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Ahmad, M.; Khan, A.; Khan, A.M.; Mazzara, M.; Distefano, S.; Sohaib, A.; Nibouche, O. Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images. Remote Sens. 2019, 11, 1136.

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