decision fusion method using an algorithm for fusion of correlated probabilities
This paper proposes a new decision fusion method accounting for conditional dependence (correlation) between land-cover classifications from multi-sensor data. The dependence structure between different classification results is calculated and used as weighting parameters for the subsequent fusion s...
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Published in | International journal of remote sensing Vol. 37; no. 1; pp. 14 - 25 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
London
Taylor & Francis
02.01.2016
Taylor & Francis Ltd |
Subjects | |
Online Access | Get full text |
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Summary: | This paper proposes a new decision fusion method accounting for conditional dependence (correlation) between land-cover classifications from multi-sensor data. The dependence structure between different classification results is calculated and used as weighting parameters for the subsequent fusion scheme. An algorithm for fusion of correlated probabilities (FCP) is adopted to fuse the prior probability, conditional probability, and obtained weighting parameters to generate a posterior probability for each class. A maximum posterior probability rule is then used to combine the posterior probabilities generated for each class to produce the final fusion result. The proposed FCP-based decision fusion method is assessed in land-cover classification over two study areas. The experimental results demonstrate that the proposed decision fusion method outperformed the existing decision fusion methods that do not take into account the correlation or dependence. The proposed decision fusion method can also be applied to other applications with different sensor data. |
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Bibliography: | http://dx.doi.org/10.1080/2150704X.2015.1109158 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1366-5901 0143-1161 1366-5901 |
DOI: | 10.1080/2150704X.2015.1109158 |