Hyperspectral image classification: A benchmark

Xudong Kang, Shutao Li, Jon Atli Benediktsson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


Hyperspectral image classification, an astonishing tool to distinguish the land covers in remote sensed hyperspectral images, has been investigated by multiple disciplines such as geoscience, environmental science, mathematics, and computer vision. Following early machine learning (e.g., support vector machines and neural networks) and feature extraction theories (e.g., principal component analysis), hundreds of hyperpsectral image classification algorithms have been proposed in order to further improve the classification accuracies. However, it is still unclear what are the real improvements of the newly proposed methods in this field or we are just fitting models to some specific data sets? To address this problem, this paper aims at discussing the major motivations and ideas in conducting a comprehensive benchmark analysis for hyperspectral image classification. The benchmark should not only allows researchers to compare their models with other algorithms but also helps identify the chief factors affecting the performance of their classification methods.

Original languageEnglish
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Cooperation for Global Awareness, IGARSS 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781509049516
Publication statusPublished - 1 Dec 2017
Event37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States
Duration: 23 Jul 201728 Jul 2017

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


Conference37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Country/TerritoryUnited States
CityFort Worth

Bibliographical note

Funding Information:
This paper is supported by the National Natural Science Foundation of China (No. 61601179), the National Natural Science Fund of China for Distinguished Young Scholars (No. 61325007), the National Natural Science Fund of China for International Cooperation and Exchanges (No. 61520106001), and the Science and Technology Plan Projects Fund of Hunan Province (No. 2015WK3001).

Publisher Copyright:
© 2017 IEEE.

Other keywords

  • Benchmark
  • Feature extraction
  • Hyperspectral image classification
  • Machine learning
  • Remote sensing


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