Multiple composite kernel learning for hyperspectral image classification

Peijun Du, Junshi Xia, Pedram Ghamisi, Akira Iwasaki, Jon Atli Benediktsson

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

4 Citations (Scopus)

Abstract

In this work, we develop a new framework to combine ensemble learning and composite kernel learning for hyperspectral image classification. We refer it as the multiple composite kernel learning, which is based on an iterative architecture. More specifically, in each iteration, we use the rotation-based ensemble to create rotation matrix, which is used to generate rotated features for both spectral and spatial information (e.g., extinction profiles). Then, the new spectral and spatial features are integrated into the composite kernels based on support vector machines classifier. Different rotation matrices will lead to obtaining various newly spectral and spatial characteristics, thereby they further increase the diversity and the classification performance. Experimental results on Indian Pines benchmark hyperspectral dataset demonstrate the excellent performance of the proposed method.

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.
Pages2223-2226
Number of pages4
ISBN (Electronic)9781509049516
DOIs
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)
Volume2017-July

Conference

Conference37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Country/TerritoryUnited States
CityFort Worth
Period23/07/1728/07/17

Bibliographical note

Funding Information:
The authors would like to thank Prof D. Landgrebe from Pur-due University for providing the image. This work was supported in part by the National Natural Science Foundation of China (Grant No. 41631176).

Publisher Copyright:
© 2017 IEEE.

Other keywords

  • Classification
  • Composite kernel learning
  • Ensemble learning
  • Hyperspectral image

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