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

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

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...

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Bibliographic Details
Main Authors: Kun Tan, Jishuai Zhu, Qian Du, Lixin Wu, Peijun Du
Format: Article
Language:English
Published: MDPI AG 2016-09-01
Series:Remote Sensing
Subjects:
classifier diversity
active learning
multi-scale homogeneity (MSH)
hyperspectral imagery
Online Access:http://www.mdpi.com/2072-4292/8/9/749

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