A decision support system for ship identification based on the curvature scale space representation

TL;DR

This paper introduces a robust decision support system for ship identification using an enhanced Curvature Scale Space representation that improves stability and accuracy in silhouette matching across various imaging modalities.

Contribution

It presents an improved CSS-based approach tracking curvature extrema and employing lobe concavity, leading to better robustness and performance in ship silhouette recognition.

Findings

High accuracy in ship identification from diverse spectral images

Enhanced robustness against silhouette variations and noise

Effective performance demonstrated on real operational imagery

Abstract

In this paper, a decision support system for ship identification is presented. The system receives as input a silhouette of the vessel to be identified, previously extracted from a side view of the object. This view could have been acquired with imaging sensors operating at different spectral ranges (CCD, FLIR, image intensifier). The input silhouette is preprocessed and compared to those stored in a database, retrieving a small number of potential matches ranked by their similarity to the target silhouette. This set of potential matches is presented to the system operator, who makes the final ship identification. This system makes use of an evolved version of the Curvature Scale Space (CSS) representation. In the proposed approach, it is curvature extrema, instead of zero crossings, that are tracked during silhouette evolution, hence improving robustness and enabling to cope successfully with cases where the standard CCS representation is found to be unstable. Also, the use of local curvature was replaced with the more robust concept of lobe concavity, with significant additional gains in performance. Experimental results on actual operational imagery prove the excellent performance and robustness of the developed method.