Better understanding of Multi Domain Battle (MDB) challenges in complex military environments may start by gaining abasic scientific appreciation of the level of generalization and scalability offered by Machine Learning (ML) solutionsdesigned, trained and optimized to achieve a single, specific task, continuously daytime and nighttime. We examine thegeneralization and scalability promises of a modern deep ML solution, applied to a unique spatial-spectral dataset thatconsists of blackbody calibrated, longwave infrared spectra of a fixed target site containing three painted metal surrogatetanks deployed in a field of mixed vegetation. Data was collected at roughly six minute intervals, nearly continuously, forover a year. This includes collection in many atmospheric conditions (rain, snow, sleet, fog, etc.) throughout the year. Thispaper focuses on data collected by a Telops Hyper-Cam from a 65 meter observation tower located at slant range of roughly550 meters, from the targets. The dataset is very complex. There are no obvious spectral signatures from the target surfaces.The complexity is due in part to the natural variations of the various types of vegetation, cloud presence, and the changingsolar loading conditions over time. This is precisely the environment MDB applications must function in. We detail someof the many training sets extracted to train different deep learning stacked auto encoder networks. We present performanceresults with receiver operator characteristic curves, confusion matrices, metric-vs-time plots, and classification maps. Weshow performance of ML models trained with data from various time windows, including over complete diurnal cyclesand their performance processing data from different days and environmental conditions.
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