The statistic results for a digital terrain model are presented that closely match measurements for 77% of the 189 possible combinations of 7 radar bands, 3 polarizations, and 9 terrain types. The model produces realistic backscatter coefficient values for the scenarios over all incidence angles from normal to grazing. The generator was created using measured data sets reported in the Handbook of Radar Scattering Statistics for Terrain covering L, C, S, X, Ka, Ku, and W frequency bands; HH, HV, and VV polarizations; and soil and rock, shrub, tree, short vegetation, grass, dry snow, wet snow, road surface, and urban area terrain types. The first two statistical moments match published values precisely, and a Chi-Square histogram test failed to reject the generator at a 95% confidence level for the 146 terrain models implemented. A Sea State model provides the grazing angle extension for predictions beyond the available measurements. This work will contain a comprehensive set of plots of mean and standard deviation versus incidence angle.
Development of phase history calibration techniques is important for improving Synthetic Aperture Radar (SAR) scene modeling capabilities. Image data of complex scene settings is used for clutter database construction, and the resulting databases are used in conjunction with synthetic radar predictions of complex targets to predict synthetic SAR imagery. The current method of trihedral calibration is typically performed after image formation, using a ratiometric technique, which is highly dependent on calibration target position and orientation and ground truth accuracy. As part of a recent SAR research data collection, measurements were made on a calibration-grade, 6-meter diameter top hat in both a homogeneous scene and with controlled obscuration and layover conditions. This paper will discuss phase-history calibration target design and scenario design to support obscuration and layover studies.
A metric is developed for evaluating performance degradation of edge detection algorithms as a function of signal to noise ratio (SNR). The metric combines both missed detections and false alarms to form a composite score. This provides a basis for objectively comparing the performance of different techniques and quantifies relative noise tolerance. It is applied to various popular algorithms, Sobel, Roberts, Prewitt, and Laplacian of Gaussian, but is described in sufficient detail to facilitate easy application to other edge detection methods. Results shown allow selection of the most optimum method for application to images with known SNR levels.
An enhanced region-growing approach for segmenting regions is introduced. A region-growing algorithm is merged with stopping criteria based on a robust noise-tolerant edge-detection routine. The region-grow algorithm is then used to segment the shadow region in a Synthetic Aperture Radar (SAR) image. This approach recognizes that SAR phenomenology causes speckle in imagery even to the shadow area due to energy injected from the surrounding clutter and target. The speckled image makes determination of edges a difficult task even for the human observer. This paper outlines the edge-enhanced region grow approach and compares the results to three other segmentation approaches including the region-grow only approach, an automated-threshold approach based on a priori knowledge of the SAR target information, and the manual segmentation approach. The comparison is shown using a tri-metric inter- algorithmic approach. The metrics used to evaluate the segmentation include percent-pixels same (PPS), the partial- directed hausdorff (PDH) metric, and a shape-based metric based on the complex inner product (CIP). Experimental results indicate that the enhanced region-growing technique is a reasonable segmentation for the SAR target image chips obtained from the Moving and Stationary Target Acquisition and Recognition (MSTAR) program.
Synthetic Aperture Radar (SAR) image scene modeling tools are of high interest to Automatic Target Recognition (ATR) algorithm evaluation because they allow the testing of ATR's over a wider range of extended operating conditions (EOCs). Typical EOCs include target aspect, target configuration, target obscuration, and background terrain variations. A baseline terrain image synthesis technique empirically derived probability density functions (pdfs) for various terrain types from measured data to allow the simulation of user defined scenes. Initial full scene simulation experiments that applied this technique to the MSTAR data showed that using measured images as a data source for creating distribution functions in artificial scenes can introduce error, unless the proper spatial autocorrelation is also modeled. Measured SAR scene pixels have non-zero autocorrelation that blurs edges between different terrain types and creates a texture in the clutter regions of the image. Unfortunately, applying simple blurring techniques, such as a moving weighted window, to model autocorrelation mutes the second and higher moments of the pixel amplitude statistics. We propose a technique that models spatial autocorrelation while preserving the desired the amplitude statistics within each defined terrain class group.
Synthetic Aperture Radar (SAR) image modeling tools are of high interest to Automatic Target Recognition (ATR) algorithm evaluation because they allow the testing of ATRs over a wider range of extended operating conditions (EOCs). Typical EOCs include target aspect, target configuration, target obscuration, and background terrain variations. Since the phenomenology fidelity of the synthetic prediction techniques is critical for ATR evaluation, metric development for complex scene prediction is needed for accurate ATR performance estimation. An image domain hybrid prediction technique involves the insertion of a synthetic target chip into a measured image background. Targets in terrain scenes will be predicted and compared with similar measured data scenarios. Shadow region histograms and terrain region histograms will be used to develop some first generation metrics for phenomenology validation of hybrid SAR prediction techniques.
A probabilistic backscatter coefficient generating function (CGF) is introduced which produces realistic backscatter coefficient values for various terrain types over all incidence angles. The CGF was developed in direct support of a multi-layer 3-D clutter modeling effort which successfully incorporated probabilistic clutter reflectivity characteristics and measured terrain elevation data to enhance clutter suppression and improve Signal-to-Clutter Ratio performance in radar applications. This probabilistic clutter modeling approach is in sharp contrast to traditional 2-D modeling techniques which typically include deterministic backscatter characteristics and assume constant terrain features within regions of interest. The functional form and parametric representation of the CGF were empirically determined by comparison with published backscatter data for nine different terrain 'types,' including, soil and rock, shrubs, trees, short vegetation, grasses, dry snow, wet snow, road surfaces, and urban areas. The statistical properties of the output, i.e., the mean and standard deviation, match published measured values to the number of significant figures reported. Likewise, the CGF output frequency of occurrence closely matches measured terrain data frequency of occurrence; a Chi-Square test fails to reject the method at a 0.05 level of significance, indicating a high level of confidence in the results. As developed, the CGF provides a computationally efficient means for incorporating probabilistic clutter characteristics into both simple and complex radar models by accurately reflecting the probabilistic scattering behavior associated with real terrain.