Hyperspectral image analysis is an important component of advanced hyperspectral image understanding. We present a new approach that identifies unique materials and the abundance of these materials in a hyperspectral image. This approach uses physical constraints on material abundances and reflectances, and avoids the presence of a dark material class by parameterizing pixel illumination. The results are optimally generated in both supervised and unsupervised modes. Applications of the image analysis approach are also presented.
We focus on the specific problem of inversion of a collection of one-dimensional projections to reconstruct a three-dimensional image of a rigid body and estimate its orientation. We assume correspondence of the points in the projections is given and derive several useful results leading to an iterative solution algorithm and a fundamental understanding of its possible scope. Finally, the algorithm was simulated on synthetic HRR data which verified its function and convergence.
The problem of predicting HRR radar and SAR signal magnitudes based on a limited number of observations is a challenging component of feature aided tracking. In this paper we describe the application of a scattering-based tomographic technique that builds persistent scatterer models of ground vehicles from a collection of HRR and/or SAR observations from varying look angles. Results are obtained using MSTAR data. Target detection results are shown using ROC curves and compared with nearest observation matching. Application of these techniques to the move-stop-move problem of vehicle tracking is also described.
This paper describes a new approach to hyperspectral image analysis using spectral signature mixture models. In this new approach spectral end-member extraction and spectral unmixing are co-dependent objectives. Previous methods tended to serialize these tasks. Our approach shows that superior hyperspectral modeling can be obtained through a parallel objective approach. The new approach also implements natural constraints on the end-members and mixtures. These constraints allow us to adopt a physical interpretation of the hyperspectral image decomposition. This new modeling technique is useful for the detection of known signatures and, more significantly, for the detection of unknown, partially occluded scene anomalies. The anomaly detection algorithm is aided by the newly developed Quad-AR filter which acts as an efficient optimal adaptive clutter rejection filter. Examples are given using a 3-band color image and 210-band HYDICE forest radiance data. The results show these new techniques to be quite effective.
KEYWORDS: 3D modeling, Data modeling, Scattering, Radon transform, Model-based design, 3D acquisition, Automatic target recognition, Statistical modeling, Radar, Linear filtering
Model-based HRR signature prediction and matching is a difficult problem due to target variations, lack of model fidelity, and sensor variations. For this reason, model-based HRR signatures often mismatch actual measured signatures. This paper introduces a novel method for predicting signatures by creating a Parametric HRR signature model from existing measured or model-based signatures. The method identifies potential three-dimensional persistent virtual scatterers and estimates their scattering patterns. The result is a parametric signature function of azimuth which better matches available signature data. The model effectively smoothes the signatures along the scatterer tracks. When used with synthetic data the technique helps eliminate model inaccuracies and uncertainties which manifest themselves in scatterer interference predictions. With sparse measured HRR profile observations, the parametric method smoothly interpolates profiles over azimuth. The parameterization method is aided by a Modified Inverse Radon Transform for persistent scatterer localization and a Fourier decomposition for scattering pattern approximation. Results using the predicted synthetic signatures in ATR multi-class problem are presented and compared with the performance based on the original synthetic and measured signatures. Results from these experiments show that this method provides a modest increase in ATR performance for synthetic signatures. The performance with sparse measured data is greatly improved and approaches dense measured data performance.
In this paper, we use an adaptive AR (Auto regressive) model to optimally filter background texture from images. The filter maps background texture into minimum variance locally white noise. Ad additive point target signal, however, is unaffected by the filter thereby effectively maximizing the signal to noise/clutter ratio. Thus, filter output thresholding can detect anomalous pixels in the images. Additionally the paper introduces a false alarm rejection scheme based on the intersection of a Four Quadrant AR (Quad-AR) filter. The paper addresses the implicit background assumptions in this approach and the median filter approach to small target detection. An example application of the filter to infrared images of missiles immersed in intense sea glint is presented. The AR filter performance is compared to a median filter performance. It is shown that for the infrared sub-pixel missile over sea problem, the Quad-AR approach is substantially better than previous approaches.
This paper addresses the use of synthetic data for air-to-air High Range Resolution (HRR) radar. Target radar models are used to generate synthetic HRR signatures in order to attempt to classify targets when there is limited real-life measured data. Target models are made up of a finite set of reflective patches. Modeling these targets is often difficult and frequently produces synthetic signatures which are not sufficiently close to measured data. We describe two approaches to improve classification of targets given limited measured HRR radar profiles and lower fidelity synthetic (model-based) profiles. The first method explores the possibility of improving model fidelity given measured HRR data. Specifically we search for material coating reflectance adjustments which consistently improve the synthetic predictions. The second approach attempts to predict missing measured data from available measured data based on global properties of the synthetic data. This is accomplished by using splines to interpolate and extrapolate between measured data and based on the global features in the synthetic data. The second method has the advantage that the model need only show global trends in the profiles over various viewing angles to aid in profile prediction. We also present algorithms for the alignment and normalization of measured data to support the above algorithms. Our results show that model corrections made by our algorithm demonstrate interpolative and extrapolative properties over regions where no measured data are available.
KEYWORDS: Cameras, Space robots, Detection and tracking algorithms, Image processing, Robotic systems, Interfaces, 3D modeling, 3D acquisition, Image segmentation, Space operations
This paper describes a robotic system which accepts motion and control commands which can be generated autonomously. The system developed has been designed to perform an autonomous grapple based on guidance control feedback provided by an image from a single camera mounted on the slave robot's end effector. The vision system consists of three parts. The first signature based, trained on an arbitrary grapple interface (i.e., no special targets are required for guidance), and provides estimates for the 3D attitude by interpolating sampled signature correlations. These signatures are essentially the distribution of line orientations obtained by radial integration of the FFT of a pre-processed edge image. The second part estimates the range and bearing of the interface based on the first and second moments of the preprocessed edge image of the interface. And the third stage of the algorithm verifies the results.
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