Vincent J. Velten

Thermophysical algebraic invariants from infrared imagery for object recognition

An important issue in developing a model-based vision system is the specification of features that are invariant to viewing and scene conditions and also specific, i.e., the feature must have different values for different classes of objects. We formulate a new approach for establishing invariant features. Our approach is unique in the field since it considers not just surface reflection and surface geometry in the specification of invariant features, but it also takes into account internal object composition and state which affect images sensed in the nonvisible spectrum. A new type of invariance called thermophysical invariance is defined. Features are defined such that they are functions of only the thermophysical properties of the imaged objects. The approach is based on a physics-based model that is derived from the principle of the conservation of energy applied at the surface of the imaged object.

Angular description for 3D scattering centers

The electromagnetic scattered field from an electrically large target can often be well modeled as if it is emanating from a discrete set of scattering centers (see Fig. 1). In the scattering center extraction tool we developed previously based on the shooting and bouncing ray technique, no correspondence is maintained amongst the 3D scattering center extracted at adjacent angles. In this paper we present a multi-dimensional clustering algorithm to track the angular and spatial behaviors of 3D scattering centers and group them into features. The extracted features for the Slicy and backhoe targets are presented. We also describe two metrics for measuring the angular persistence and spatial mobility of the 3D scattering centers that make up these features in order to gather insights into target physics and feature stability. We find that features that are most persistent are also the most mobile and discuss implications for optimal SAR imaging.

Dominant-subspace invariants

Object recognition requires robust and stable features that are unique in feature space. Lie group analysis provides a constructive procedure to determine such features, called invariants, when they exist. Absolute invariants are rare in general, so quasi-invariants relax the restrictions required for absolute invariants and, potentially, can be just as useful in real-world applications. The paper develops the concept of a dominant-subspace invariant, a particular type of quasi-invariant, using the theory of Lie groups. A constructive algorithm is provided that fundamentally seeks to determine an integral submanifold which, in practice, is a good approximation to the orbit of the Lie group action. This idea is applied to the long-wave infrared problem and experimental results are obtained supporting the approach. Other application areas are cited.

Interference-invariant target detection in hyperspectral images

In this paper we address the problem of detecting targets in hyperspectral images when the target signature is buried in random noise and interference (from other materials in the same pixel). We assume that the hyperspectral pixel measurement is a linear combination of the target and interference signatures observed in additive noise. The linear mixing assumption leads to a linear vector space interpretation of the measurement vector, which can be decomposed into a noise-only subspace and a target-plus- interference subspace. While it is true that the target and interference subspaces are orthogonal to the noise-only subspace, the target subspace and interference subspace are, in general, not orthogonal. The non-orthogonality between the target and interference subspaces results in leakage of interference signals into the output of matched filters resulting in false detections (i.e., higher false alarm rates). In this paper, we replace the Matched Filer Detector (MFD), which is based on orthogonal projections, with a Matched Subspace Detector (MSD), which is built on non- orthogonal or oblique projections. The advantage of oblique projections is that they eliminate the leakage of interference signals into the detector, thereby making detectors based on oblique projections invariant to the amount of interference. Furthermore, under Gaussian assumptions for the additive noise, it has been shown that the MSD is Uniformly Most Powerful (higher probability of detect for a fixed probability of false alarm) among all detectors that share this invariance to interference power. In this paper we evaluate the ability of two versions of the MSD to detect targets in HYDICE data collected over sites A and B located at the U.S. Army Yuma proving grounds. We compute data derived receiver operating characteristics (ROC) curves and show that the MSD out- performs the MFD.

Angular tracking of 3D scattering center using an optical flow algorithm

In radar cross section studies it is well known that the EM scattered field from an electrically large target can often be well modeled as if it is emanating from a discrete set of points on the target called scattering centers. In this paper, we presented an algorithm to perform angular tracking of 3D scattering centers extracted using the SBR (shooting and bouncing ray) technique. The angular tracking algorithm used the optical flow based velocity fields in conjunction with the other attributes available during the 3D extractions to determine angular associations. The feature tracks can then be grown from these associations. Preliminary results on simple targets shows that this methodology works well. This algorithm can also be extended to track limited angular occlusion of features

SAR image invariants from 3D scattering centers

Synthetic Aperture Radar (SAR) sensors have many advantages over electro-optical sensors (EO) for target recognition applications, such as range-independent resolution and superior poor weather performance. However, the relative unavailability of SAR data to the basic research community has retarded analysis of the fundamental invariant properties of SAR sensors relative to the extensive invariant literature for EO, and in particular photographic sensors. Prior work that was reported at this conference has developed the theory of SAR invariants based on the radar scattering center concept and provided several examples of invariant configurations of SAR scatterers from measured and synthetic SAR image data. This paper will show that invariant scattering configurations can be extracted from predicted 3D data scatterer data and used to predict invariant features in measured SAR image data.

Comparison of quantitative analysis techniques to discriminate intimately mixed bidirectional laboratory reflectance spectra

In this paper we evaluate the ability of the Matched Subspace Detector (MSD), Matched Filter Detector (MFD) and Orthogonal Subspace Projection (OSP) to discriminate material types in laboratory samples of intimately mixed bidirectional reflectance data. The analysis consists of a series of experiments where bidirectional reflectance spectra of intimate mixtures of enstatite-olivine and anorthite-olivine in various proportions are converted to single scattering albedo (SSA) using Hapkes model for bidirectional reflectance. The linearized SSA spectra are used as inputs to the various detectors and the output for each is evaluated as a function of the proportion of target- to-interference. Results are presented as a series of figures that show overall the MSD has a higher target-to- background separation (i.e., better class separation) than either the MFD or OSP. This target-to-background separation results in fewer false alarms for the MSD than either of the other two detectors.

Parallax mitigation for hyperspectral change detection

A pixel-level Generalized Likelihood Ratio Test (GLRT) statistic for hyperspectral change detection is developed to mitigate false change caused by image parallax. Change detection, in general, represents the difficult problem of discriminating significant changes opposed to insignificant changes caused by radiometric calibration, image registration issues, and varying view geometries. We assume that the images have been registered, and each pixel pair provides a measurement from the same spatial region in the scene. Although advanced image registration methods exist that can reduce mis-registration to subpixel levels; residual spatial mis-registration can still be incorrectly detected as significant changes. Similarly, changes in sensor viewing geometry can lead to parallax error in an urban cluttered scene where height structures, such as buildings, appear to move. Our algorithm looks to the inherent relationship between the image views and the theory of stereo vision to perform parallax mitigation leading to a search result in the assumed parallax direction. Mitigation of the parallax-induced false alarms is demonstrated using hyperspectral data in the experimental analysis. The algorithm is examined and compared to the existing chronochrome anomalous change detection algorithm to assess performance.

Synthetic aperature radar (SAR) geometric invariants: experimental results

Synthetic Aperture Radar (SAR) sensors have many advantages over electro-optical sensors (EO) for target recognition applications, such as range-independent resolution and superior poor weather performance. However, the relative unavailability of SAR data to the basic research community has retarded analysis of the fundamental invariant properties of SAR sensors relative to the extensive invariant literature for EO, and in particular photographic sensors. Prior work that was reported at this conference has developed the theory of SAR invariants based on the radar scattering center concept. This paper will give several examples of invariant configurations of SAR scatterers from measured SAR image data.

Vehicle classification for civilian and non-civilian applications: A survey

The ability to classify a vehicle is of extreme importance for both civilian and non-civilian applications. For non-civilian applications the state-of-the-art leaves much to be desired, as hierarchal and real-time classification have yet to be truly investigated. This paper provides a survey of the current state-of-the-art in vehicle classification and provides recommendations for future research areas to advance the current capabilities.