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

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.