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.
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