To improve cutting-edge deep learning techniques for more relevant defense applications, we extend our wellestablished port monitoring ATR techniques from generic ship classes to a pair of newly curated datasets: aircraft carriers and other military ships. We explore several techniques for data augmentation and splits to represent different deployment regimes, such as revisiting known military ports and new observations of never-before-seen ports and ships. We see reliable results (F1 <0.9) detecting and classifying aircraft carriers by type–and by proxy, nationality–as well as encouraging preliminary results (mAP <0.7) detecting and differentiating military ships by sub-class.
We present a method for monitoring rapidly urbanizing areas with deep learning techniques. This method was generated during participation in the SpaceNet7 deep learning challenge and utilizes a U-Net architecture for semantically labeling each frame in a time series of monthly images that span roughly two years. The image sequences were collected over one hundred rapidly urbanizing regions. We discuss our network architecture and post processing algorithms for combining multiple semantically labeled frames to provide object level change detection.
For many intelligence sources, reliable independent algorithms exist for interpreting the data and reporting relevant information to analysts. However, achieving the necessary cross-source data fusion from these sources and algorithmic outputs to achieve true sensemaking can be challenging. This is especially true at the individual object level, given the sources' highly variable spatiotemporal resolutions and uncertainties. We have developed a framework for merging automatic target recognition (ATR) algorithms and their outputs to produce a sensor-agnostic means of object level change detection to establish the necessary patterns-of-life for big picture sensemaking, activity-based intelligence, and autonomous decision making.
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