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12 April 2021 Effects of image degradation on algorithm training and performance
Kimberly Manser, Shreya Ramesh, Bassam Bahhur
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Proceedings Volume 11729, Automatic Target Recognition XXXI; 117290S (2021) https://doi.org/10.1117/12.2586804
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
Experimental results are presented from an investigation that evaluated the effects of introducing degraded imagery into the training and test sets of an algorithm. Degradation consisted of various applied MTFs (blur) and noise profiles. The hypothesis was that the introduction of degraded imagery into the training set would increase the algorithm's accuracy when degraded imagery was present in the test set. Preliminary experimentation confirmed this hypothesis, with some additional observations regarding robustness and feature selection for degraded imagery. Further investigations are suggested to advance this work, including increased variety of objects for classification, additional wave bands, and randomized degradations.
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Kimberly Manser, Shreya Ramesh, and Bassam Bahhur "Effects of image degradation on algorithm training and performance", Proc. SPIE 11729, Automatic Target Recognition XXXI, 117290S (12 April 2021); https://doi.org/10.1117/12.2586804
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KEYWORDS
Feature selection
Modulation transfer functions
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