Iterative morphological algorithms for automated detection of land mines

Sinan Batman; John Ioannis Goutsias

doi:10.1117/12.396176

22 August 2000 Iterative morphological algorithms for automated detection of land mines

Sinan Batman, John Ioannis Goutsias

Author Affiliations +

Proceedings Volume 4038, Detection and Remediation Technologies for Mines and Minelike Targets V; (2000) https://doi.org/10.1117/12.396176
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

A new hybrid algorithm, based on combining the decorrelating and packing qualitites of Principal Component (PC) analysis and the shape extracting and filtering properties of Mathematical Morphology, is investigated in the frame-work of land mien detection. The new method is similar in spirit to the MM-MNF algorithm, which is based on a linear pre- filter, followed by a morphological multispectral detection component (MM). The new filter (PC-MM), has a similar concatenated structure, and addresses some of the weaknesses inherent in the linear component of the MM-MNF algorithm; namely, the susceptibility of the MNF transform to clutter inhomogeneity, as well as to variation sin clutter covariance estimation. The PC-MM algorithm addresses the stationarity problem by solely operating on image peaks extracted by a morphological top-hat transform. Therefore, the algorithm is much less susceptible to the present of different textural regions. Subsequently, the peaks in the extracted multispectral top-het image are projected into uncorrelated bands using the principal component (PC) transform. Due to the packing property of the PC transform, the target markers are typically found in the first and second bands in the PC transformed image. The targets are then detected using a variant of the morphological detection scheme. The new method provides a fast and satisfactory first-pass detection result, for images of different clutter homogeneities and target types. The extracted targets, from the first pass, are then issued to improve the detection result in a subsequent iteration, by updating covariance estimates of relevant filter variables.

Citation Download Citation

Sinan Batman and John Ioannis Goutsias "Iterative morphological algorithms for automated detection of land mines", Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); https://doi.org/10.1117/12.396176

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