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Remote Sens. 2016, 8(9), 749; doi:10.3390/rs8090749

A Novel Tri-Training Technique for Semi-Supervised Classification of Hyperspectral Images Based on Diversity Measurement

1,†
,
1,†
,
2,* , 1
and
3,*
1
Jiangsu Key laboratory of Resources and Environment Information Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
2
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
3
Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China
*
Authors to whom correspondence should be addressed.
Academic Editors: András Jung, Lenio Soares Galvao and Prasad S. Thenkabail
Received: 27 June 2016 / Revised: 2 September 2016 / Accepted: 4 September 2016 / Published: 12 September 2016
View Full-Text   |   Download PDF [5163 KB, uploaded 12 September 2016]   |  

Abstract

This paper introduces a novel semi-supervised tri-training classification algorithm based on diversity measurement for hyperspectral imagery. In this algorithm, three measures of diversity, i.e., double-fault measure, disagreement metric and correlation coefficient, are applied to select the optimal classifier combination from different classifiers, e.g., support vector machine (SVM), multinomial logistic regression (MLR), extreme learning machine (ELM) and k-nearest neighbor (KNN). Then, unlabeled samples are selected using an active learning (AL) method, and consistent results of any other two classifiers combined with a spatial neighborhood information extraction strategy are employed to predict their labels. Moreover, a multi-scale homogeneity (MSH) method is utilized to refine the classification result with the highest accuracy in the classifier combination, generating the final classification result. Experiments on three real hyperspectral data indicate that the proposed approach can effectively improve classification performance. View Full-Text
Keywords: classifier diversity; active learning; multi-scale homogeneity (MSH); hyperspectral imagery classifier diversity; active learning; multi-scale homogeneity (MSH); hyperspectral imagery
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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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MDPI and ACS Style

Tan, K.; Zhu, J.; Du, Q.; Wu, L.; Du, P. A Novel Tri-Training Technique for Semi-Supervised Classification of Hyperspectral Images Based on Diversity Measurement. Remote Sens. 2016, 8, 749.

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