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4 May 2009 A general purpose adaptive approach to image classification, automatic target detection, and recognition for multispectral imagery
Beato T. Cheng
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Proceedings Volume 7335, Automatic Target Recognition XIX; 73350D (2009) https://doi.org/10.1117/12.818973
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States
Abstract
Automatic target detection and recognition (ATD/ATR) can be considered one of the most sought after goals in image exploitation. There are no shortage of "good" algorithms in ATD/ATR on paper, yet the problem remains that an algorithm cannot be applied directly to different scenario and expect a similar success rate. One can attribute the difficulties in ATD/ATR to the ambiguity of the definition of "target", and the specific choice of image data and parameters in the design of the algorithms. We propose a general purpose approach to the problem in that we do not specify what a target is, except that it will be chosen by a user from a number of detected anomalies at the end of the classification cycle. At this time, a user can specify a number of attributes to be associated with a candidate target. There is a learning phase where the algorithm and the discriminating parameters are tuned based on the characteristics of the image data and the classification methods. There are a number of attributes associated with a target, both in spectral and spatial values, which can be set by a user. The number of bands used for input can be varied; however it is limited to three to seven bands at this point. Target recognition is achieved when a target candidate has a passing figure of merit, which again is defined by the user. It is hoped that this approach can provide a framework of ATD/ATR with greatest flexibility in algorithm re-use.
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Beato T. Cheng "A general purpose adaptive approach to image classification, automatic target detection, and recognition for multispectral imagery", Proc. SPIE 7335, Automatic Target Recognition XIX, 73350D (4 May 2009); https://doi.org/10.1117/12.818973
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KEYWORDS
Target recognition
Detection and tracking algorithms
Target detection
Image classification
Image processing
Algorithm development
Image segmentation
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