End-to-end machine learning for co-optimized sensing and automated target recognition

Scott McCloskey

doi:10.1117/12.3023229

8 June 2024 End-to-end machine learning for co-optimized sensing and automated target recognition

Scott McCloskey

Proceedings Volume 13039, Automatic Target Recognition XXXIV; 130390F (2024) https://doi.org/10.1117/12.3023229
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Many sensors produce data that rarely, if ever, is viewed by a human, and yet sensors are often designed to maximize subjective image quality. For sensors whose data is intended for embedded exploitation, maximizing the subjective image quality to a human will generally decrease the performance of downstream exploitation. In recent years, computational imaging researchers have developed end-to-end learning methods that co-optimize the sensing hardware with downstream exploitation via end-to-end machine learning. This talk will describe two such approaches at Kitware. In the first, we use an end-to-end ML approach to design a multispectral sensor that’s optimized for scene segmentation and, in the second, we optimize post-capture super-resolution in order to improve the performance of airplane detection in overhead imagery.

Conference Presentation

Citation Download Citation

Scott McCloskey "End-to-end machine learning for co-optimized sensing and automated target recognition", Proc. SPIE 13039, Automatic Target Recognition XXXIV, 130390F (8 June 2024); https://doi.org/10.1117/12.3023229

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