This paper presents initial object profile classification results using range and elevation independent features from a simulated infrared profiling sensor. The passive infrared profiling sensor was simulated using a LWIR camera. A field data collection effort to yield profiles of humans and animals is reported. Range and elevation independent features based on height and width of the objects were extracted from profiles. The profile features were then used to train and test four classification algorithms to classify objects as humans or animals. The performance of Naïve Bayesian (NB), Naïve Bayesian with Linear Discriminant Analysis (LDA+NB), K-Nearest Neighbors (K-NN), and Support Vector Machines (SVM) are compared based on their classification accuracy. Results indicate that for our data set SVM and (LDA+NB) are capable of providing classification rates as high as 98.5%. For perimeter security applications where misclassification of humans as animals (true negatives) needs to be avoided, SVM and NB provide true negative rates of 0% while maintaining overall classification rates of over 95%.
This paper presents object profile classification results using range and speed independent features from an infrared profiling sensor. The passive infrared profiling sensor was simulated using a LWIR camera. Field data collected near the US-Mexico border to yield profiles of humans and animals is reported. Range and speed independent features based on height and width of the objects were extracted from profiles. The profile features were then used to train and test three classification algorithms to classify objects as humans or animals. The performance of Naïve Bayesian (NB), K-Nearest Neighbors (K-NN), and Support Vector Machines (SVM) are compared based on their classification accuracy. Results indicate that for our data set all three algorithms achieve classification rates of over 98%. The field data is also used to validate our prior data collections from more controlled environments.
The physical model for long wave infrared (LWIR) thermal imaging through a dust obscurant incorporates transmission loss as well as an additive path radiance term, both of which are dependent on an obscurant density along the imaging path. When the obscurant density varies in time and space, the desired signal is degraded by two anti-correlated atmospheric noise components-the transmission (multiplicative) and the path radiance (additive)-which are not accounted for by a single transmission parameter. This research introduces an approach to modeling the performance impact of dust obscurant variations. Effective noise terms are derived for obscurant variations detected by a sensor via a forward radiometric analysis of the imaging context. The noise parameters derived here provide a straightforward approach to predicting imager performance with existing NVESD models such as NVThermIP.
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