TRACKING INFORMATION IN SAR IMAGE FORMATION AND CLASSIFICATION ALGORITHMS
Traditional target detection pipelines involve two sequential steps: the formation of a range-profile or likely-image, and the classification of likely targets within that image. Although it has been shown that target tracking in the RaDAR image-domain can be unnecessarily noisy, with more accurat...
| Main Authors: | , , , |
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| Other Authors: | |
| Language: | en_US |
| Published: |
International Foundation for Telemetering
2017
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| Online Access: | http://hdl.handle.net/10150/626979 http://arizona.openrepository.com/arizona/handle/10150/626979 |
| Summary: | Traditional target detection pipelines involve two sequential steps: the formation of a range-profile
or likely-image, and the classification of likely targets within that image. Although it has been
shown that target tracking in the RaDAR image-domain can be unnecessarily noisy, with more accurate
and efficient implementations involving a direct analysis of the measured wavefield, image
formation remains a desirable output in many applications due to its highly descriptive and interpretable
nature. In this paper, we outline a mechanism for formalizing and accelerating this procedure
in application-specific use cases. Enabled by recent advances in deep learning, we present a
pipeline for automatically selecting an “optimal” filtered back-projection model, forming a likelyimage,
and performing target recognition and classification. The architecture allows practitioners
to track and optimize the flow of information throughout the pipeline, enabling applications that
utilize only intermediate outputs of the algorithm. |
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