Synthetic data is often leveraged for training and testing Automatic Target Recognition (ATR) systems on a variety of operating conditions (OCs). Existing mechanisms for creating the sampling distribution of OCs to generate this data are difficult to visualize, modify, and extend. To address this, we created a user interface and toolchain for multi-modal OC sampling from probabilistic graphical models (PGMs). Our web browser-based interface allows for visualizing the PGMs, modifying the conditional probability distributions of their nodes, importing and exporting their state, operation in single- or multi-modality configurations, and persisting generated samples to a relational database. The Vue-driven web interface, programmatic interface, as well as the Python machinery for OC sampling and persistence have been containerized to allow for simplified deployment and distribution. The work described here supplies a new baseline for OC generation for single- and multi-sensor simulation and fusion.
Decision level fusion algorithms combine separate classification scores of a test sample to make a unified class declaration. The aim of decision level fusion algorithms is to achieve better classification performance by combining decisions rather than picking the single best performing algorithm. Multi-modal fusion can be achieved by fusing scores of deep learning models trained on different sensing modalities and tested on a target imaged from co-located sensors. Given differences in phenomenology, fusing EO sensors with SAR may boost performance when extended operating conditions are detrimental to the performance of one modality over the other. The EO modalities discussed in this work (VNIR and MWIR) are susceptible to the time of day while SAR is robust to time of day. Conversely, SAR returns of a target can vary greatly when aspect angle changes while EO modalities are relatively robust. This work analyzes the effectiveness of decision level fusion algorithms on MWIR, VNIR, and SAR modalities given disparate times of day and collection aspects.
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