Publications

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Fast, accurate and robust automatic target recognition (ATR) in optical aerial imagery can provide game-changing advantages to military commanders and personnel. ATR algorithms must reject non-targets with a high degree of confidence in a world with an infinite number of possible input images. Furthermore, they must learn to recognize new targets without requiring massive data collections. Whereas most machine learning algorithms classify data in a closed set manner by mapping inputs to a fixed set of training classes, open set recognizers incorporate constraints that allow for inputs to be labelled as unknown. We have adapted two template-based open set recognizers to use computer generated synthetic images of military aircraft as training data, to provide a baseline for military-grade ATR: (1) a frequentist approach based on probabilistic fusion of extracted image features, and (2) an open set extension to the one-class support vector machine (SVM). These algorithms both use histograms of oriented gradients (HOG) as features as well as artificial augmentation of both real and synthetic image chips to take advantage of minimal training data. Our results show that open set recognizers trained with synthetic data and tested with real data can successfully discriminate real target inputs from non-targets. However, there is still a requirement for some knowledge of the real target in order to calibrate the relationship between synthetic template and target score distributions. We conclude by proposing algorithm modifications that may improve the ability of synthetic data to represent real data.

Except in extreme weather conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. SAR can provide surveillance by making multiple passes over a wide area. For object-based intelligence, it is convenient to use these multiple passes to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call "static-features." Our approach is unique in that we have multiple SAR passes of an area over a long period of time (on the order of weeks). From these many SAR images of the same area, we can combine SAR images from different times to create a variety of SAR products. For example, we introduce a novel SAR image product that captures how different regions decorrelate at different rates. From these many SAR products, we exact superpixels or groups of connected pixels that describe a homogenous region. Using pixels contained within a superpixel we develop a series of one-class classification algorithms using a goodness-of-fit metric that classifies terrains of interest in each SAR product for each superpixel. To combine the results from many SAR products we use P-value fusion. The result is a classification and a confidence about the different classes. To enforce spatial consistency, we represent the confidence labeling of the superpixels as a conditional random field and infer the most likely labeling by maximize the posterior probability of the random field. The result is a colorized SAR image where each color represents a different terrain class.

Abstract not provided.

Synthetic aperture radar (SAR) is a remote sensing technology that can truly operate 24/7. It's an all-weather system that can operate at any time except in the most extreme conditions. By making multiple passes over a wide area, a SAR can provide surveillance over a long time period. For high level processing it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call 'static features.' In this paper we concentrate on automatic road segmentation. This not only serves as a surrogate for finding other static features, but road detection in of itself is important for aligning SAR images with other data sources. In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. We also show how a modified Kolmogorov-Smirnov test can be used to model the static features even when the independent observation assumption is violated.

Except in the most extreme conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. A SAR can provide surveillance over a long time period by making multiple passes over a wide area. For object-based intelligence it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call “static features.” In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. Using superpixels and their first two moments we develop a series of one-class classification algorithms using a goodness-of-fit metric. P-value fusion is used to combine the results from different classes. We also show how to combine multiple one-class classifiers to get a confidence about a classification. This can be used by downstream algorithms such as a conditional random field to enforce spatial constraints.

Current techniques for building detection in Synthetic Aperture Radar (SAR) imagery can be computationally expensive and/or enforce stringent requirements for data acquisition. We present a technique that is effective and efficient at determining an approximate building location from multi-pass single-pol SAR imagery. This approximate location provides focus-of-attention to specific image regions for subsequent processing. The proposed technique assumes that for the desired image, a preprocessing algorithm has detected and labeled bright lines and shadows. Because we observe that buildings produce bright lines and shadows with predetermined relationships, our algorithm uses a graph clustering technique to find groups of bright lines and shadows that create a building. The nodes of the graph represent bright line and shadow regions, while the arcs represent the relationships between the bright lines and shadow. Constraints based on angle of depression and the relationship between connected bright lines and shadows are applied to remove unrelated arcs. Once the related bright lines and shadows are grouped, their locations are combined to provide an approximate building location. Experimental results are presented to demonstrate the outcome of this technique.

Current techniques for building detection in Synthetic Aperture Radar (SAR) imagery can be computationally expensive and/or enforce stringent requirements for data acquisition. The desire is to present a technique that is effective and efficient at determining an approximate building location. This approximate location can be used to extract a portion of the SAR image to then perform a more robust detection. The proposed technique assumes that for the desired image, bright lines and shadows, SAR artifact effects, are approximately labeled. These labels are enhanced and utilized to locate buildings, only if the related bright lines and shadows can be grouped. In order to find which of the bright lines and shadows are related, all of the bright lines are connected to all of the shadows. This allows the problem to be solved from a connected graph viewpoint. Where the nodes are the bright lines and shadows and the arcs are the connections between bright lines and shadows. Constraints based on angle of depression and the relationship between connected bright lines and shadows are applied to remove unrelated arcs. Once the related bright lines and shadows are grouped, their locations are combined to provide an approximate building location. Experimental results are provided showing the outcome of the technique.