Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning
Joint Authors
Chen, Liwei
Wang, Tieshen
Zhu, Haifeng
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-05
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Subpixel mapping (SPM) algorithms effectively estimate the spatial distribution of different land cover classes within mixed pixels.
This paper proposed a new subpixel mapping method based on image structural self-similarity learning.
Image structure self-similarity refers to similar structures within the same scale or different scales in image itself or its downsampled image, which widely exists in remote sensing images.
Based on the similarity of image block structure, the proposed method estimates higher spatial distribution of coarse-resolution fraction images and realizes subpixel mapping.
The experimental results show that the proposed method is more accurate than existing fast subpixel mapping algorithms.
American Psychological Association (APA)
Chen, Liwei& Wang, Tieshen& Zhu, Haifeng. 2017. Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1190678
Modern Language Association (MLA)
Chen, Liwei…[et al.]. Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning. Mathematical Problems in Engineering No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1190678
American Medical Association (AMA)
Chen, Liwei& Wang, Tieshen& Zhu, Haifeng. Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1190678
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190678