Automatic target recognition (ATR) algorithms that rely on machine learning approaches are limited by the quality of the training dataset and the out-of-domain performance. The performance of a two-step ATR algorithm (ATR-EnvI) that on fusing thermal imagery with environmental data is investigated using thermal imagery containing buried and surface object collected in New Hampshire, Mississippi, Arizona, and Panama. An autoencoder neural network is used to encode the salient environmental conditions for a given climatic condition into an environmental feature vector. The environmental feature vector allows for the inclusion of environmental data with varying dimensions to robustly treat missing data. Using this architecture, we evaluate the performance of the two-step ATR on a test dataset collected in an unseen climatic condition, e.g., tropical wet climate when the training dataset contains imagery collected in a similar condition, e.g., subtropical, and dissimilar climates. Lastly, we evaluate the impact of including physics-based synthetic training data has on performance for out-of-domain climates.
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.
The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a dataset that consists of thermal images containing buried objects paired with the corresponding environmental and meteorological conditions, we employ a machine learning approach to determine which environmental conditions are the most impactful on the visibility of the buried objects. We find the key environmental conditions include incoming short-wave solar radiation, soil volumetric water content, and average air temperature. For each image, ROIs are computed using a computer vision approach and these ROIs are coupled with the most important environmental conditions to form the input for the classification algorithm. The environmentally informed classification algorithm produces a decision on whether the ROI contains a buried object by simultaneously learning on the ROIs with a classification neural network and on the environmental data using a tabular neural network. On a given set of ROIs, we have shown that the environmentally informed classification approach improves the detection of buried objects within the ROIs.
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