| Autor: |
Matthew D. Reisman, Taber Fisher, Jonathan J. Dalrymple, Garrett Zans, Adam G. Francisco |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Automatic Target Recognition XXXI. |
| DOI: |
10.1117/12.2587102 |
| Popis: |
Deep learning-based classification of objects in overhead imagery is a difficult problem to solve due to low to moderate available resolution as well as wide ranges of scale between objects. Traditional machine learning object classification techniques yield sub-optimal results in this scenario, with new techniques developed to optimize performance. Our Lockheed Martin team has developed data pre-processing techniques such as context masking and uniform rotation which improve classifier performance in this application. Additionally, we have demonstrated that shallow classifier models perform at least as well as deeper models in this paradigm, allowing for fast training and inference times. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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