Mine detection via generalized Wilcoxon-Mann-Whitney classification

Carey E. Priebe; Lenore J. Cowen

doi:10.1117/12.324260

Abstract

This paper presents a nonparametric classification procedure based on a generalization of classical rank-based statistics and a preliminary investigation of the method's applicability to mine detection. The classifier is particularly relevant to high-dimensional applications and can improve performance characteristics such as discriminatory power. The common practice of considering ranked interpoint distances is generalized to point-to-subset distances and a recurrence for the exact distribution of this generalized Wilcoxon-Mann- Whitney (GWMW) test statistic is available. From a classification standpoint, GWMW represents a class of generalized weighted (k,l)-nearest neighbor rules. The GWMW classifier is applied to multispectral minefield data collected under the The Coastal Battlefield Reconnaissance and Analysis (COBRA) Program. A truthed detection map obtained from the multispectral image set and provided by NSWC Coastal Systems Station, Panama City, Florida, contains both true mines and false positives. The GWMW classifier is compared to classical classification methods on this data via cross- validation. The preliminary experimental results indicate that GWMW yields a significant improvement in discriminatory power for this important practical application.

Citation Download Citation

Carey E. Priebe, Carey E. Priebe, Lenore J. Cowen, Lenore J. Cowen, } "Mine detection via generalized Wilcoxon-Mann-Whitney classification", Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); doi: 10.1117/12.324260; https://doi.org/10.1117/12.324260

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