Title
Improvement of the Example-Regression-Based Super-Resolution Land Cover Mapping Algorithm
Abstract
Super-resolution mapping (SRM) is a method for generating a fine-resolution land cover map from coarse-resolution fraction images. Example-regression-based SRM algorithms can estimate a fine-resolution land cover map with detailed spatial information by learning land cover spatial patterns from available land cover maps. Existing example-regression-based SRM algorithms are sensitive to fraction errors, and the results often include many linear artifacts and speckles. To overcome these shortcomings, this study proposes an improved example-regression-based SRM algorithm. The objective function of the proposed SRM algorithm comprises three terms. The first term is used to minimize the difference between the fraction values of the estimated fine-resolution land cover map and the input fraction values. The second term is used to maximize the class membership possibility values of the fine pixels in the result. The final term is used to make the result locally smooth. The proposed SRM algorithm is compared with several popular SRM algorithms using both synthetic and real fraction images. Experimental results indicate that the proposed SRM algorithm can produce results with less speckles and linear artifacts, more spatial details, smoother boundaries, and higher accuracies than the SRM results used for comparison.
Year
DOI
Venue
2015
10.1109/LGRS.2015.2423496
Geoscience and Remote Sensing Letters, IEEE
Keywords
Field
DocType
example based,subpixel mapping (spm),super-resolution mapping (srm),support vector regression (svr),satellites,accuracy,optimization,remote sensing,earth,spatial resolution
Spatial analysis,Satellite,Speckle pattern,Regression,Remote sensing,Pixel,Land cover,Spatial ecology,Image resolution,Mathematics
Journal
Volume
Issue
ISSN
12
8
1545-598X
Citations 
PageRank 
References 
4
0.41
8
Authors
4
Name
Order
Citations
PageRank
Yihang Zhang1868.80
Du, Y.240.41
Feng Ling320921.29
Xiaodong Li417116.82