Image fusion for context-aided automatic target recognition

Erik Blasch; Zheng Liu; Yufeng Zheng

doi:10.1117/12.2564876

19 May 2020 Image fusion for context-aided automatic target recognition

Erik Blasch, Zheng Liu, Yufeng Zheng

Proceedings Volume 11394, Automatic Target Recognition XXX; 113940U (2020) https://doi.org/10.1117/12.2564876
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Automatic Target Recognition (ATR) has seen many recent advances from image fusion, machine learning, and data collections to support multimodal, multi-perspective, and multi-focal day-night robust surveillance. This paper highlights ideas, strategies, and concepts as well as provides an example for electro-optical and infrared image fusion cooperative intelligent ATR analysis. The ATR results support simultaneous tracking and identification for physicsbased and human-derived information fusion (PHIF). The importance of context serves as a guide for ATR systems and determines the data requirements for robust training in deep learning approaches.

Conference Presentation

Citation Download Citation

Erik Blasch, Zheng Liu, and Yufeng Zheng "Image fusion for context-aided automatic target recognition", Proc. SPIE 11394, Automatic Target Recognition XXX, 113940U (19 May 2020); https://doi.org/10.1117/12.2564876

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