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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 9858531, 9 pages
Research Article

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia
3Commonwealth Scientific and Industrial Research Organization (CSIRO) Land and Water, Canberra, ACT 2601, Australia

Correspondence should be addressed to Linyi Li; nc.ude.uhw@iynilil

Received 13 April 2017; Accepted 8 June 2017; Published 6 July 2017

Academic Editor: Silvia Conforto

Copyright © 2017 Linyi Li et al. 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.


In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.