Leveraging environmental conditions to inform a two-step ATR for buried objects

Sophia Potoczak Bragdon; Vuong H. Truong; Megan I. Bishop; Jay L. Clausen

doi:10.1117/12.2663970

13 June 2023 Leveraging environmental conditions to inform a two-step ATR for buried objects

Sophia Potoczak Bragdon, Vuong H. Truong, Megan I. Bishop, Jay L. Clausen

Proceedings Volume 12521, Automatic Target Recognition XXXIII; 125210M (2023) https://doi.org/10.1117/12.2663970
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The detection and classification of buried objects utilizing long wave infrared (LWIR) imaging is a challenging task. The ability to detect a buried object is reliant on discriminating background noise from surface temperature anomalies induced by the presence of a foreign object below ground surface. The presence of background noise and temperature anomalies in LWIR images containing buried objects is correlated to the ambient environmental conditions. For example, increased solar loading of the soil can lead to increased background noise, while increased volumetric water content of the soil can mask the presence of temperature anomalies due to buried objects. This paper discusses advancements to a proposed environmentally informed two-step automatic target recognition (ATR) algorithm for buried objects and the characterization of environmental phenomenology corresponding to buried objects and background noise. The detection step of the algorithm is based on an edge detection approach and is designed to maximize probability of detection while ignoring the false alarm rate. The classification step filters the false alarms from the true alarms utilizing a novel framework that combines the environmental conditions with the LWIR imagery. The environmentally informed classification algorithm concurrently reasons from a set of environmental conditions recorded by sensors coupled with a region of interest detected in the first step. The classification algorithm combines a CNNbased image machine learning algorithm with a fully connected neural network to extract features on the coupled environmental and image data to ultimately produce a classification. The performance of the algorithm is compared to common machine learning ATRs.

Conference Presentation

Citation Download Citation

Sophia Potoczak Bragdon, Vuong H. Truong, Megan I. Bishop, and Jay L. Clausen "Leveraging environmental conditions to inform a two-step ATR for buried objects", Proc. SPIE 12521, Automatic Target Recognition XXXIII, 125210M (13 June 2023); https://doi.org/10.1117/12.2663970

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