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Remote Sens. 2016, 8(10), 804;

Incorporating Diversity into Self-Learning for Synergetic Classification of Hyperspectral and Panchromatic Images

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Authors to whom correspondence should be addressed.
Academic Editors: Naser El-Sheimy, Zahra Lari, Adel Moussa, Gonzalo Pajares Martinsanz, Xiaofeng Li and Prasad S. Thenkabail
Received: 15 July 2016 / Revised: 8 September 2016 / Accepted: 22 September 2016 / Published: 29 September 2016
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Derived from semi-supervised learning and active learning approaches, self-learning (SL) was recently developed for the synergetic classification of hyperspectral (HS) and panchromatic (PAN) images. Combining the image segmentation and active learning techniques, SL aims at selecting and labeling the informative unlabeled samples automatically, thereby improving the classification accuracy under the condition of small samples. This paper presents an improved synergetic classification scheme based on the concept of self-learning for HS and PAN images. The investigated scheme considers three basic rules, namely the identity rule, the uncertainty rule, and the diversity rule. By integrating the diversity of samples into the SL scheme, a more stable classifier is trained by using fewer samples. Experiments on three synthetic and real HS and PAN images reveal that the diversity criterion can avoid the problem of bias sampling, and has a certain advantage over the primary self-learning approach. View Full-Text
Keywords: hyperspectral; self-learning; semi-supervised; active learning; synergetic classification hyperspectral; self-learning; semi-supervised; active learning; synergetic classification

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lu, X.; Zhang, J.; Li, T.; Zhang, Y. Incorporating Diversity into Self-Learning for Synergetic Classification of Hyperspectral and Panchromatic Images. Remote Sens. 2016, 8, 804.

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