Evaluating subpixel target detection algorithms in hyperspectral imagery

Yuval Cohen, Yitzhak August, Dan G. Blumberg, Stanley R. Rotman

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


Our goal in this work is to demonstrate that detectors behave differently for different images and targets and to propose a novel approach to proper detector selection. To choose the algorithm, we analyze image statistics, the target signature, and the targets physical size, but we do not need any type of ground truth. We demonstrate our ability to evaluate detectors and find the best settings for their free parameters by comparing our results using the following stochastic algorithms for target detection: the constrained energy minimization (CEM), generalized likelihood ratio test (GLRT), and adaptive coherence estimator (ACE) algorithms. We test our concepts by using the dataset and scoring methodology of the Rochester Institute of Technology (RIT) Target Detection Blind Test project. The results show that our concept correctly ranks algorithms for the particular images and targets including in the RIT dataset.

Original languageEnglish
Article number103286
JournalJournal of Electrical and Computer Engineering
StatePublished - 8 Oct 2012

ASJC Scopus subject areas

  • Signal Processing
  • Computer Science (all)
  • Electrical and Electronic Engineering


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