A physics-based approach to detecting and classifying surface and sub-surface objects in longwave (thermal) infrared imagery is described. The main premise is to associate a heat capacity and effective depth with each voxel (or segment) in the image. An energy budget for the voxel then leads to a linear, first-order differential equation, in which the temperature is forced by fluxes in and out of the voxel (shortwave solar radiation, longwave radiation, sensible and latent turbulent heat exchanges with the atmosphere), while relaxing towards an equilibrium temperature determined by a weighted mean of the air and ground temperatures. Next, it is shown how this simplified model can be incorporated into maximum-likelihood and Bayesian classifiers to distinguish buried objects from their surroundings. In particular, a version of the Bayesian classifier is formulated that leverages the differing amplitude and phase response of a buried object over the diurnal cycle. These classifiers will be tested on experimental data in future work.
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.
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