Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/617941
Title: Learning-Based Superresolution Land Cover Mapping
Authors: Feng Ling;Yihang Zhang;Giles M. Foody;Xiaodong Li;Xiuhua Zhang;Shiming Fang;Wenbo Li;Yun Du
subject: patch pairs|Learning database|neighboring patches|superresolution mapping (SRM)
Year: 2016
Publisher: IEEE
Abstract: Superresolution mapping (SRM) is a technique for generating a fine-spatial-resolution land cover map from coarse-spatial-resolution fraction images estimated by soft classification. The prior model used to describe the fine-spatial-resolution land cover pattern is a key issue in SRM. Here, a novel learning-based SRM algorithm, whose prior model is learned from other available fine-spatial-resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine- and coarse-spatial-resolution representation for the same area. From the learning database, patch pairs that have similar coarse-spatial-resolution patches as those in the input fraction images are selected. Fine-spatial-resolution patches in these selected patch pairs are then used to estimate the latent fine-spatial-resolution land cover map by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA's National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and kappa values in all of these SRM algorithms, by using the entire maps in the accuracy assessment.
URI: http://localhost/handle/Hannan/148390
http://localhost/handle/Hannan/617941
ISSN: 0196-2892
1558-0644
volume: 54
issue: 7
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7434025.pdf2.85 MBAdobe PDFThumbnail
Preview File
Title: Learning-Based Superresolution Land Cover Mapping
Authors: Feng Ling;Yihang Zhang;Giles M. Foody;Xiaodong Li;Xiuhua Zhang;Shiming Fang;Wenbo Li;Yun Du
subject: patch pairs|Learning database|neighboring patches|superresolution mapping (SRM)
Year: 2016
Publisher: IEEE
Abstract: Superresolution mapping (SRM) is a technique for generating a fine-spatial-resolution land cover map from coarse-spatial-resolution fraction images estimated by soft classification. The prior model used to describe the fine-spatial-resolution land cover pattern is a key issue in SRM. Here, a novel learning-based SRM algorithm, whose prior model is learned from other available fine-spatial-resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine- and coarse-spatial-resolution representation for the same area. From the learning database, patch pairs that have similar coarse-spatial-resolution patches as those in the input fraction images are selected. Fine-spatial-resolution patches in these selected patch pairs are then used to estimate the latent fine-spatial-resolution land cover map by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA's National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and kappa values in all of these SRM algorithms, by using the entire maps in the accuracy assessment.
URI: http://localhost/handle/Hannan/148390
http://localhost/handle/Hannan/617941
ISSN: 0196-2892
1558-0644
volume: 54
issue: 7
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7434025.pdf2.85 MBAdobe PDFThumbnail
Preview File
Title: Learning-Based Superresolution Land Cover Mapping
Authors: Feng Ling;Yihang Zhang;Giles M. Foody;Xiaodong Li;Xiuhua Zhang;Shiming Fang;Wenbo Li;Yun Du
subject: patch pairs|Learning database|neighboring patches|superresolution mapping (SRM)
Year: 2016
Publisher: IEEE
Abstract: Superresolution mapping (SRM) is a technique for generating a fine-spatial-resolution land cover map from coarse-spatial-resolution fraction images estimated by soft classification. The prior model used to describe the fine-spatial-resolution land cover pattern is a key issue in SRM. Here, a novel learning-based SRM algorithm, whose prior model is learned from other available fine-spatial-resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine- and coarse-spatial-resolution representation for the same area. From the learning database, patch pairs that have similar coarse-spatial-resolution patches as those in the input fraction images are selected. Fine-spatial-resolution patches in these selected patch pairs are then used to estimate the latent fine-spatial-resolution land cover map by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA's National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and kappa values in all of these SRM algorithms, by using the entire maps in the accuracy assessment.
URI: http://localhost/handle/Hannan/148390
http://localhost/handle/Hannan/617941
ISSN: 0196-2892
1558-0644
volume: 54
issue: 7
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7434025.pdf2.85 MBAdobe PDFThumbnail
Preview File