Paper Info
Title
Improving Super-Resolution Mapping by Combining Multiple Realizations Obtained Using the Indicator-Geostatistics Based Method.
Abstract
Indicator-geostatistics based super-resolution mapping (IGSRM) is a popular super-resolution mapping (SRM) method. Unlike most existing SRM methods that produce only one SRM result each, IGSRM generates multiple equally plausible super-resolution realizations (i.e., SRM results). However, multiple super-resolution realizations are not desirable in many applications, where only one SRM result is usually required. These super-resolution realizations may have different strengths and weaknesses. This paper proposes a novel two-step combination method of generating a single SRM result from multiple super-resolution realizations obtained by IGSRM. In the first step of the method, a constrained majority rule is proposed to combine multiple super-resolution realizations generated by IGSRM into a single SRM result under the class proportion constraint. In the second step, partial pixel swapping is proposed to further improve the SRM result obtained in the previous step. The proposed combination method was evaluated for two study areas. The proposed method was quantitatively compared with IGSRM and Multiple SRM (M-SRM), an existing multiple SRM result combination method, in terms of thematic accuracy and geometric accuracy. Experimental results show that the proposed method produces SRM results that are better than those of IGSRM and M-SRM. For example, in the first example, the overall accuracy of the proposed method is 7.43-10.96% higher than that of the IGSRM method for different scale factors, and 1.09-3.44% higher than that of the M-SRM, while, in the second example, the improvement in overall accuracy is 2.42-4.92%, and 0.08-0.90%, respectively. The proposed method provides a general framework for combining multiple results from different SRM methods.
Year
DOI
Venue
2017
10.3390/rs9080773
REMOTE SENSING
Keywords
Field
DocType
super-resolution mapping,indicator geostatistics,class proportion constraint,pixel swapping,land cover classification
Computer vision,Swap (computer programming),Mathematical optimization,Super resolution mapping,Algorithm,Pixel,Thematic map,Artificial intelligence,Majority rule,Geostatistics,Mathematics
Journal
Volume
Issue
ISSN
9
8
2072-4292
Citations 
PageRank 
References 
2
0.40
19
Authors
6
Name
Order
Citations
PageRank
Zhongkui Shi131.08
Peijun Li2379.63
Huiran Jin382.64
Yugang Tian432.79
Yan Chen514545.22
Xianfeng Zhang6668.92