Abstract
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 target′s 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.
Highlights
One would like to choose a hyperspectral detection algorithm for use in a particular scenario with the assurance that it would be “optimal,” that is, that the type of algorithm to be used and its free parameters would be optimized for the particular task for which it is being considered
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
Because the responses of these algorithms can vary depending on target placement, we adapted the Rotman-Bar Tal Algorithm (RBTA) [1] for comparing point target detection algorithms, used for infrared broadband images, to the analysis of hyperspectral imagery [2,3,4]
Summary
One would like to choose a hyperspectral detection algorithm for use in a particular scenario with the assurance that it would be “optimal,” that is, that the type of algorithm to be used and its free parameters would be optimized for the particular task for which it is being considered In such cases, the complexity of real-world scenarios and the difficulties of predicting the exact target signature in situ, make it hard to believe that we can predict the optimal target detection algorithm ahead of time.
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