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Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis
Joint Authors
Cao, Min
Ming, Dongping
Xu, Lu
Fang, Ju
Liu, Lin
Ling, Xiao
Ma, Weizhi
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-09-16
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Image texture is an important visual cue in image processing and analysis.
Texture feature expression is an important task of geo-objects expression by using a high spatial resolution remote sensing image.
Texture features based on gray level co-occurrence matrix (GLCM) are widely used in image spatial analysis where the spatial scale is especially of great significance.
Based on the Fourier frequency-spectral analysis, this paper proposes an optimal scale selection method for GLCM.
Different subset textures are firstly upscaled by GLCM with different window sizes.
Then the multiscale texture feature images are converted into the frequency domain by Fourier transform.
Consequently, the radial distribution and angular distribution curves changing with different window sizes from spectrum energy can be achieved, by which the texture window size can be selected.
In order to verify the validity of this proposed texture scale selection method, this paper uses high-resolution fusion images to classify land cover based on multiscale texture expression.
The results show that the proposed method combining frequency-spectral analysis-based texture scale selection can guarantee the quality and accuracy of the classification, which further proves the effectiveness of optimal texture window size selection method bases on frequency spectrum analysis.
Other than scale selection in spatial domain, this paper casts a novel idea for texture scale selection in the frequency domain, which is meant for scale processing of remote sensing image.
American Psychological Association (APA)
Cao, Min& Ming, Dongping& Xu, Lu& Fang, Ju& Liu, Lin& Ling, Xiao…[et al.]. 2019. Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis. Journal of Spectroscopy،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1192052
Modern Language Association (MLA)
Cao, Min…[et al.]. Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis. Journal of Spectroscopy No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1192052
American Medical Association (AMA)
Cao, Min& Ming, Dongping& Xu, Lu& Fang, Ju& Liu, Lin& Ling, Xiao…[et al.]. Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis. Journal of Spectroscopy. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1192052
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192052