I just presented two paper at the IEEE/MTS Oceans Conference Biloxi, MS. I have attached the slides and paper below for those who might want to read it.
The first paper was entitled “Multi-Platform Target Detection using Multi-Channel Coherence Analysis and Robustness to the Effects of Disparity”
Abstract
The use of multiple disparate platforms in many remote sensing and surveillance applications allows one to exploit the coherent information shared among all sensory systems thereby potentially reducing the risk of making single-sensory biased detection and classification decisions. This paper introduces a target detection method based upon multi-channel coherence analysis (MCA) framework which optimally decomposes the multi-channel data to analyze their linear dependence or coherence. This decomposition then allows one to extract MCA features that can be used to implement a coherence-based detector. This detector is applied to a data set of simulated disparate sonar imagery provided by the Naval Surface Warfare Center (NSWC) – Panama City. This database contains images of both targets and non-targets with various variabilities with respect to resolution, signal-to-noise ratio (SNR), target and non-target types, etc. Sensitivity analyses are then carried out in order to gauge the performance under such variablities that may be encountered in disparate multi-platform detection problems. Performance of the detection method will be given in terms of probability of detection (Pd), probability of false alarm (Pfa), and the receiver operating characteristic (ROC) curves.
Oceans09 Multiplatform Paper
Oceans09 Multiplatform Slides
The first paper was entitled “An Underwater Target Detection System for Electro-Optical Imagery Data”
Abstract
The problem of detecting underwater targets from Electro-optical (EO) images is considered in this paper. A block-based log-likelihood ratio test has been developed for detection and segmentation of underwater mine-like objects in the EO images captured with a CCD-based image sensor. The main focus of this research is to develop a robust detection algorithm that can be used to detect low contrast and partial underwater objects from the EO imagery with low false alarm rate. The detection method involves identifying frames of interest (FOI) containing the potential targets. Once the FOI have been identified, regions of interest (ROI) within the FOI are segmented from the background. Performance of the detection method is tested in terms of probability of detection, false alarm rate, and receiver operating characteristic (ROC) curves for FOI in the selected data runs. The algorithm shows promising results in target detection and generation of good silhouettes for subsequent classification.
Oceans09 EO Paper
Oceans09 EO Slides
Derek
