Lance M. Kaplan

Extended fractal analysis for texture classification and segmentation

The Hurst parameter for two-dimensional (2-D) fractional Brownian motion (fBm) provides a single number that completely characterizes isotropic textured surfaces whose roughness is scale-invariant. Extended self-similar (ESS) processes were previously introduced in order to provide a generalization of fBm. These new processes are described by a number of multiscale Hurst parameters. In contrast to the single Hurst parameter, the extended parameters are able to characterize a greater variety of natural textures where the roughness of these textures is not necessarily scale-invariant. In this work, we evaluate the effectiveness of multiscale Hurst parameters as features for texture classification and segmentation. For texture classification, the performance of the generalized Hurst features is compared to traditional Hurst and Gabor features. Our experiments show that classification accuracy for the generalized Hurst and Gabor features are comparable even though the generalized Hurst features lower the dimensionality by a factor of five. Next, the segmentation accuracy using generalized and standard Hurst features is evaluated on images of texture mosaics. For these experiments, the performance is evaluated with and without supplemental contrast and average grayscale features. Finally, we investigate the effectiveness of the Hurst features to segment real synthetic aperture radar (SAR) imagery.

Maximum likelihood methods for bearings-only target localization

We develop four maximum likelihood (ML) methods to localize a moving target using a network of acoustical sensor arrays. Each array transmits a direction-of-arrival (DOA) estimate to a central processor, which employs one of the localization techniques. The four ML approaches use different target signal models where the time retardation factor for the target position and the degradation of the target signal through the air may or may not be included in the model. We compare these methods along with a linear least squares approach through a number of simulations at various signal to noise levels.

Improved SAR target detection via extended fractal features

The utility of the extended fractal (EF) feature is evaluated for the enhancement of the focus of attention (FOA) stage of a synthetic aperture radar (SAR) automatic target recognition (ATR) system. Unlike more traditional SAR detection features that distinguish target pixels from the background only on the basis of contrast, the EF feature is sensitive to both the contrast and size of objects. Furthermore, the structure for the EF feature computational algorithm lends itself to very fast implementation, and it can be shown that the new feature has a CFAR-like (constant false alarm rate) property. We demonstrate the improved performance using the new feature by testing a number of different detection approaches over two databases of SAR imagery.

Fast texture database retrieval using extended fractal features

The increase in the number of multimedia databases consisting of images has created a need for a quick method to search these databases for a particular type of image. An image retrieval system will output images from the database similar to the query image in terms of shape, color, and texture. For the scope of our work, we study the performance of multiscale Hurst parameters as texture features for database image retrieval over a database consisting of homogeneous textures. These extended Hurst features represent a generalization of the Hurst parameter for fractional Brownian motion (fBm) where the extended parameters quantize the texture roughness of an image at various scales. We compare the retrieval performance of the extended parameters against traditional Hurst features and features obtained from the Gabor wavelet. Gabor wavelets have previously been suggested for image retrieval applications because they can be tuned to obtain texture information for a number of different scales and orientations. In our experiments, we form a database combining textures from the Bonn, Brodatz, and MIT VisTex databases. Over the hybrid database, the extended fractal features were able to retrieve a higher percentage of similar textures than the Gabor features. Furthermore, the fractal features are faster to compute than the Gabor features.

Bearings-only target localization for an acoustical unattended ground sensor network

This paper extends our development of acoustical bearings-only target localization for the case of multiple moving targets. The resulting techniques can be used to locate and track targets traveling through a network of acoustical sensor arrays. Each array computes and transmits multiple direction-of-arrival (DOA) estimates to a central processor, which employs the target localization technique. In previous work, we developed ML techniques that may or may not account for the fact that a bearing measurement points to the location of a moving target at a retarded time. By inserting a simple bearings association computation in the ML methods, we define quasi-ML techniques that can estimate the location and velocity of multiple targets using multiple bearing estimates per a sensor array.

Analysis of multiplicative speckle models for template-based SAR ATR

It has been noted that signal aperture radar (SAR) imagery of clutter exhibits Rayleigh multiplicative noise due to speckle. We use a database of MSTAR target chips to verify that the noise is multiplicative rather than additive for all regions in the chip. Then, by examining histograms corresponding to the noise residuals, we show that a Weibull distribution that is almost Rayleigh best fits the data. However, when we restrict the analysis over the target region, the log-normal and Rayleigh models fit the noise equally as well. This can be attributed to scattering mechanisms that are unstable over five degrees of aspect angle.

Prescreening during image formation for ultrawideband radar

Standard radar image formation techniques waste computational resources by full resolving all areas of the scene, even regions of benign clutter. We introduce a multiscale prescreener algorithm that runs as part of the image formation processing step for ultrawideband (UWB) synthetic aperture radar (SAR) systems. The prescreener processes intermediate radar data generated by a quadtree backprojection image former. As the quadtree algorithm iterates, it is resolving increasingly finer subpatches of the scene. After each quadtree stage, the prescreener makes an estimate of the signal-to-background ratio of each subpatch and applies a constant false alarm rate (CFAR) detector to decide which ones might contain a target of interest. Whenever the prescreener determines that a subpatch is not near a detection, it cues the image former to terminate further processing of that subpatch. Using a small database of UWB radar field data, we demonstrate that the prescreener is able to decrease the overall computational load of the image formation process. We also show that the new multiscale prescreener method produces fewer false alarms than the conventional two-parameter CFAR prescreener applied to the completely formed image.

Hyperspectral image segmentation using filter banks for texture augmentation

This paper presents a method for appending texture information to existing hyperspectral data to increase classification accuracy. The features extracted for texture classification are based on the subbands of various configurations of the octave-band directional filter bank. This filter bank represents a computationally efficient alternative to other 2-D decompositions, and it is able to divide frequency space into equivalent and meaningful partitions. Results using different radial and angular resolutions are presented, and the different filter bank configurations are compared and discussed with respect to other decompositions.

Detection of targets in low-resolution FLIR images using two-dimensional directional wavelets

This paper investigates the use of Continuous Wavelet Transform (CWT) features for detection of targets in low resolution FLIR imagery. We specifically use the CWT features corresponding to the integration of target features at all relevant scales and orientations. These features are combined with non-linear transformations (thresholding, enhancement, morphological operations). We compare our previous results using the Mexican hat wavelet with those obtained using the two types of directional wavelets: the Morlet wavelet and the Cauchy wavelets. The algorithm was tested on the TRIM2 data base.

Effect of signal-to-clutter ratio on template-based ATR

In this work, we evaluate the robustness of template matching schemes for automatic target recognition (ATR) against the effects of clutter layover. The results of our experiments indicate the performance of template matching ATR in various image transform domains against the signal to clutter ratio (SCR). The purpose of these transforms is to enhance the target features in a chip while suppressing features representative of background clutter or simple noise. The ATR experiments were performed for synthetic aperture radar imagery using target chips in the public domain MSTAR database. The transforms include pointwise nonlinearities such as the logarithm and power operations. The templates are generated using the training portion of the MSTAR database at the nominal SCR. Many different ATR parameterizations are considered for each transform domain where templates are built to represent different ranges of aspect angles in uniform angular bins of 5, 10, 15, 30, and 45 degree increments. The different ATRs were evaluated using the testing portion of the database where synthetic clutter was added to lower the SCR.