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. Coherent change detection (CCD) in SAR can identify minute changes such as vehicle tracks that occur between images taken at different times. From polarimetric SAR capabilities, researchers have developed decompositions that allow one to automatically classify the scattering type in a single polarimetric SAR (PolSAR) image set. We extend that work to CCD in PolSAR images to identify the type change. Such as change caused by no return regions, trees, or ground. This work could then be used as a preprocessor for algorithms to automatically detect tracks.
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.
As unmanned systems (UMS) proliferate for security and defense applications, autonomous control system capabilities that enable them to perform tactical operations are of increasing interest. These operations, in which UMS must match or exceed the performance and speed of people or manned assets, even in the presence of dynamic mission objectives and unpredictable adversary behavior, are well beyond the capability of even the most advanced control systems demonstrated to date. In this paper we deconstruct the tactical autonomy problem, identify the key technical challenges, and place them into context with the autonomy taxonomy produced by the US Department of Defense's Autonomy Community of Interest. We argue that two key capabilities beyond the state of the art are required to enable an initial fieldable capability: rapid abstract perception in appropriate environments, and tactical reasoning. We summarize our work to date in tactical reasoning, and present initial results from a new research program focused on abstract perception in tactical environments. This approach seeks to apply semantic labels to a broad set of objects via three core thrusts. First, we use physics-based multi-sensor fusion to enable generalization from imperfect and limited training data. Second, we pursue methods to optimize sensor perspective to improve object segmentation, mapping and, ultimately, classification. Finally, we assess the potential impact of using sensors that have not traditionally been used by UMS to perceive their environment, for example hyperspectral imagers, on the ability to identify objects. Our technical approach and initial results are presented.
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.
Coherent change detection (CCD) can indicate subtle scene changes in synthetic aperture radar (SAR) imagery, such as vehicle tracks. Automatic track detection in SAR CCD is difficult due to various sources of low coherence other than the track activity we wish to detect. Existing methods require user cues or explicit modeling of track structure, which limit algorithms' ability to find tracks that do not fit the model. In this paper, we present a track detection approach based on a pixel-level labeling of the image via a conditional random field classifier, with features based on radial derivatives of local Radon transforms. Our approach requires no modeling of track characteristics and no user input, other than a training phase for the unary cost of the conditional random field. Experiments show that our method can successfully detect both parallel and single tracks in SAR CCD as well as correctly declare when no tracks are present.
Coherent change detection (CCD) images, which are prod- ucts of combining two synthetic aperture radar (SAR) images taken at different times of the same scene, can reveal subtle sur- face changes such as those made by tire tracks. These images, however, have low texture and are noisy, making it difficult to au- Tomate track finding. Existing techniques either require user cues and can only trace a single track or make use of templates that are difficult to generalize to different types of tracks, such as those made by motorcycles, or vehicles sizes. This paper presents an approach to automatically identify vehicle tracks in CCD images. We identify high-quality track segments and leverage the con- strained Delaunay triangulation (CDT) to find completion track segments. We then impose global continuity and track smoothness using a binary random field on the resulting CDT graph to determine edges that belong to real tracks. Experimental results show that our algorithm outperforms existing state-of-the- Art techniques in both accuracy and speed.
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.
Combining multiple synthetic aperture radar (SAR) images taken at different times of the same scene produces coherent change detection (CCD) images that can detect small surface changes such as tire tracks. The resulting CCD images can be used in an automated approach to identify and label tracks. Existing techniques have limited success due to the noisy nature of these CCD images. In particular, existing techniques require some user cues and can only trace a single track. This paper presents an approach to automatically identify and label multiple tracks in CCD images. We use an explicit objective function that utilizes the Bayesian information criterion to find the simplest set of curves that explains the observed data. Experimental results show that it is capable of identifying tracks under various scenes and can correctly declare when no tracks are present.
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.