Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-06
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Linyi& Xu, Tingbao& Chen, Yun. 2017. Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141318
Modern Language Association (MLA)
Li, Linyi…[et al.]. Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1141318
American Medical Association (AMA)
Li, Linyi& Xu, Tingbao& Chen, Yun. Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141318
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141318