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Survey of supervised classification techniques for hyperspectral images

Qingchen Qiu (School of Information Science and Engineering, Shandong University, Jinan, China)
Xuelian Wu (Qilu Hospital, Shandong University, Jinan, China)
Zhi Liu (School of Information Science and Engineering, Shandong University, Jinan, China)
Bo Tang (Shandong University, Jinan, China)
Yuefeng Zhao (Shandong Normal University, Jinan, China)
Xinyi Wu (Shandong University, Jinan, China)
Hongliang Zhu (Information Security Centre, Beijing University of Posts and Telecommunications, Beijing, China)
Yang Xin (Centre of Information Security, Beijing University of Posts and Telecommunications, Beijing, China)

Sensor Review

ISSN: 0260-2288

Article publication date: 19 June 2017

Abstract

Purpose

This paper aims to provide a framework of the supervised hyperspectral classification, to study the traditional flowchart of hyperspectral image (HIS) analysis and processing. HSI technology has been proposed for many years, and the applications of this technology were promoted by technical advancements.

Design/methodology/approach

First, the properties and current situation of hyperspectral technology are summarized. Then, this paper introduces a series of common classification approaches. In addition, a comparison of different classification approaches on real hyperspectral data is conducted. Finally, this survey presents a discussion on the classification results and points out the classification development tendency.

Findings

The core of this survey is to review of the state of the art of the classification for hyperspectral images, to study the performance and efficiency of certain implementation measures and to point out the challenges still exist.

Originality value

The study categorized the supervised classification for hyperspectral images, demonstrated the comparisons among these methods and pointed out the challenges that still exist.

Keywords

Acknowledgements

Qingchen Qiu and Xuelian Wu contributed equally to this paper. This work was supported by the Fundamental Research Funds of Shandong University (No. 2015JC038).

Citation

Qiu, Q., Wu, X., Liu, Z., Tang, B., Zhao, Y., Wu, X., Zhu, H. and Xin, Y. (2017), "Survey of supervised classification techniques for hyperspectral images", Sensor Review, Vol. 37 No. 3, pp. 371-382. https://doi.org/10.1108/SR-07-2016-0124

Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited