A generalized image scene decomposition-based system for supervised classification of very high resolution remote sensing imagery

Zhi Yong Lv, Haiqing He, Jón Atli Benediktsson, Hong Huang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Very high resolution (VHR) remote sensing images are widely used for land cover classification. However, to the best of our knowledge, few approaches have been shown to improve classification accuracies through image scene decomposition. In this paper, a simple yet powerful observational scene scale decomposition (OSSD)-based system is proposed for the classification of VHR images. Different from the traditional methods, the OSSD-based system aims to improve the classification performance by decomposing the complexity of an image's content. First, an image scene is divided into sub-image blocks through segmentation to decompose the image content. Subsequently, each sub-image block is classified respectively, or each block is processed firstly through an image filter or spectral-spatial feature extraction method, and then each processed segment is taken as the feature input of a classifier. Finally, classified sub-maps are fused together for accuracy evaluation. The effectiveness of our proposed approach was investigated through experiments performed on different images with different supervised classifiers, namely, support vector machine, k-nearest neighbor, naive Bayes classifier, and maximum likelihood classifier. Compared with the accuracy achieved without OSSD processing, the accuracy of each classifier improved significantly, and our proposed approach shows outstanding performance in terms of classification accuracy.

Original languageEnglish
Article number814
JournalRemote Sensing
Volume8
Issue number10
DOIs
Publication statusPublished - Oct 2016

Bibliographical note

Funding Information:
The authors would like to thank the editor-in-chief, the anonymous associate editor, and the reviewers for their insightful comments and suggestions. This work was supported by the Geographic National Condition Monitoring Engineering Research Center of Sichuan Province (GC201515), a project of the China Postdoctoral Science Foundation (2015M572658XB), and the Visiting Scholar Foundation of Key Laboratory of Optoelectronic Technology and Systems (Chongqing University), Ministry of Education. The National Natural Science Foundation of China (41401526) and the Open Research Fund of Key Laboratory ofWatershed Ecology and Geographical Environment Monitoring, NASG (WE2015003) also supported this work.

Publisher Copyright:
© 2016 by the authors.

Other keywords

  • Image classification
  • Image decomposition
  • Observational scene
  • Very high resolution

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