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Remote Sens. 2016, 8(10), 814; doi:10.3390/rs8100814

A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery

1
School of Computer Science and Engineering, Xi’An University of Technology, Xi’an 710048, China
2
Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, National Administration of Surveying, Mapping and Geoinformation, Nanchang 330013, China
3
School of Geomatics, East China University of Technology, Nanchang 330013, China
4
Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik IS 107, Iceland
5
Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yuei-An Liou, Chyi-Tyi Lee, Yuriy Kuleshov, Jean-Pierre Barriot, Chung-Ru Ho, Guoqing Zhou, Xiaofeng Li and Prasad S. Thenkabail
Received: 25 July 2016 / Revised: 30 August 2016 / Accepted: 26 September 2016 / Published: 30 September 2016
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
View Full-Text   |   Download PDF [35058 KB, uploaded 30 September 2016]   |  

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. View Full-Text
Keywords: observational scene; image decomposition; very high resolution; image classification observational scene; image decomposition; very high resolution; image classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lv, Z.; He, H.; Benediktsson, J.A.; Huang, H. A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery. Remote Sens. 2016, 8, 814.

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