Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning

Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning

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 with...

Full description

Bibliographic Details
Main Authors: Liwei Chen, Tieshen Wang, Haifeng Zhu
Format: Article
Language:English
Published: Hindawi Limited 2017-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2017/5254024
id doaj-3f1b19c5d7d847d6a6ee4146305a0c44
record_format Article
spelling doaj-3f1b19c5d7d847d6a6ee4146305a0c442020-11-25T00:08:47ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472017-01-01201710.1155/2017/52540245254024Subpixel Mapping Algorithms Based on Block Structural Self-Similarity LearningLiwei Chen0Tieshen Wang1Haifeng Zhu2Harbin Engineering University, Harbin 150001, ChinaHarbin Engineering University, Harbin 150001, ChinaHarbin Engineering University, Harbin 150001, ChinaSubpixel 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.http://dx.doi.org/10.1155/2017/5254024
collection DOAJ
language English
format Article
sources DOAJ
author Liwei Chen
Tieshen Wang
Haifeng Zhu
spellingShingle Liwei Chen
Tieshen Wang
Haifeng Zhu
Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning
Mathematical Problems in Engineering
author_facet Liwei Chen
Tieshen Wang
Haifeng Zhu
author_sort Liwei Chen
title Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning
title_short Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning
title_full Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning
title_fullStr Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning
title_full_unstemmed Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning
title_sort subpixel mapping algorithms based on block structural self-similarity learning
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2017-01-01
description 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.
url http://dx.doi.org/10.1155/2017/5254024
work_keys_str_mv AT liweichen subpixelmappingalgorithmsbasedonblockstructuralselfsimilaritylearning
AT tieshenwang subpixelmappingalgorithmsbasedonblockstructuralselfsimilaritylearning
AT haifengzhu subpixelmappingalgorithmsbasedonblockstructuralselfsimilaritylearning
_version_ 1725414577401757696

Similar Items