Optimal trade-off distance classifier correlation filters (OTDCCFs) for synthetic aperture radar automatic target recognition (SAR ATR)
| Autor: | Bhagavatula Vijaya Kumar, Daniel W. Carlson, Robert Mitchell, Michael Hoffelder |
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| Rok vydání: | 1997 |
| Předmět: |
Synthetic aperture radar
Mahalanobis distance Contextual image classification business.industry fungi ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition body regions Correlation symbols.namesake ComputingMethodologies_PATTERNRECOGNITION Automatic target recognition Fourier transform Geography symbols Computer vision Artificial intelligence skin and connective tissue diseases business Optical filter Classifier (UML) |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.281548 |
| Popis: | Recent developments in optimal trade-off based composite correlation filter methods have improved the recognition and classification of an object over a range of image distortions. We extend the capability of the distance classifier correlation filter introduced by Mahalanobis et al by using he optimal trade-off between different correlation criteria. These correlation filters can be used for the automatic target cueing or recognition of synthetic aperture radar (SAR) images. In this paper we will present results of designing these distortion-tolerant filters with simulated SAR imagery and testing with simulated SAR target images inserted into real SAR backgrounds. |
| Databáze: | OpenAIRE |
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