NDLI: Context-Dependent Fusion for mine detection using Airborne Hyperspectral Imagery

Content Provider	IEEE Xplore Digital Library
Author	Lijun Zhang Frigui, H. Gader, P. Bolton, J.
Copyright Year	2009
Description	Author affiliation: CISE Dept., University of Florida, Gainesville, 32611, USA (Gader, P.; Bolton, J.) \|\| CECS Dept., University of Louisville, KY 40292, USA (Lijun Zhang; Frigui, H.)
Abstract	We present a method for fusing the decisions of multiple algorithms that use different hyperspectral imagery (HI) classification methods and apply it to mine detection. The proposed fusion method, called Cumulative Separation-Based (CSB) method, is embedded into our Context-Dependent Fusion for Multiple Algorithms(CDF-MA) framework. The CDF-MA is motivated by the fact that the relative performance of different algorithms can vary significantly depending on the type of the different targets and other environmental conditions. Results on real world HI data show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our initial experiments have also indicated that the proposed method outperforms all individual algorithms and the global weighted average fusion method.
Starting Page	1
Ending Page	4
File Size	470419
Page Count	4
File Format	PDF
ISBN	9781424446865
DOI	10.1109/WHISPERS.2009.5288973
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-26
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mine detection Hyperspectral sensors Error analysis Hyperspectral imagery Ensemble classifiers Probability distribution Remote sensing Context-dependent fusion Clustering algorithms Training data Random variables Region 4 Hyperspectral imaging Testing
Content Type	Text
Resource Type	Article

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