Wolfgang Kober

A fast adaptive algorithm for image restoration

An adaptive filtering algorithm based on an Euclidean direction search (EDS) method is presented for image restoration. It is a fast algorithm and has a computational complexity of O(N) for least squares optimization. Computer simulations illustrate that this algorithm is very effective in image restoration. The figures for signal-to-noise ratio improvement (SNRI) produced by this algorithm are comparable to those obtained by using the recently reported sample-based conjugate gradient (SCG) algorithm, which has a computational complexity of O(N/sup 2/). This algorithm can also be extended to other applications in adaptive signal processing.

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.

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.

Invariant methods for indexing and relative reconstruction of 3D models using 1D HRR and 2D SAR

We describe a geometric model of high-resolution radar (HRR), where objects being imaged by the sensor are assumed to consists of a collection of isotropic scattering centers distributed in three dimensions. Three, four, five and six point pure HRR invariant quantities for non-coplanar reflecting centers are presented. New work showing invariants combining HRR and SAR measurements are then presented. All these techniques require matching corresponding features in multiple HRR and/or SAR views. These features are represented using analytic scattering models. Multiple features within the same HRR resolution cell can be individually detected and separated using interference-suppression filters. These features can then be individually tracked to maintain correspondence as the object poise changes. We validate our HRR/SAR invariants using the XPATCH simulation system. Finally, a view-based method for 3D model reconstruction is developed and demonstrated.

Group target tracking using game theory

The problem of tracking of a group of targets is considered in this paper. We will present an overview of an investigation into this problem by first using the targets velocity state vectors covariance matrix to establish target grouping and then by exploiting concepts derived from game theory, in particular the leader-follower techniques, and graph theory to represent and establish relationships that influence the tracking of objects that belong to a group formation.

Canonical correlation analysis of LWIR imagery in the frequency domain

This paper presents a linear system approximation for automated analysis of passive, long-wave infrared (LWIR) imagery. The approach is based on the premise that for a time varying ambient temperature field, the ratio of object surface temperature to ambient temperature is independent of amplitude and is a function only of frequency. Thus, for any given material, it is possible to compute a complex transfer function in the frequency domain with real and imaginary parts that are indicative of the material type. Transfer functions for a finite set of ordered points on a hypothesized object create an invariant set for that object. This set of variates is then concatenated with another set of variates (obtained either from the same object or a different object) to form two random complex vectors. Statistical tests of affine independence between the two random vectors is facilitated by decomposing the generalized correlation matrix into canonical form and testing the hypothesis that the sample canonical correlations are all zero for a fixed probability of false alarm (PFA). In the case of joint Gaussian distributions, the statistical test is a maximum likelihood. Results are presented using real images.