Principles of Modern Radar - Volume 2 1891121537

648 CHAPTER 14 Automatic Target RecognitionScattering centers can account for a large percentage of the overall energy in animage of a target [47]. The scattering center attributes from synthetic SAR images oftargets that are larger than a few wavelengths can be determined using the geometrictheory of diffraction model [47]. Attributes of interest include location, fully polarimetricamplitudes, aspect-dependent amplitude tapers and geometric type [47]. These featuresshould be augmented by other features, such as texture features or shadow, when used forATR [47].14.4.4.6 Evolutionary ProgrammingEvolutionary programming can be used as a model optimization technique in ATR througha defined number of mutation phases on the parameters of interest. For example, an objectmay be represented as a small set of spatially-localized filters. Evolutionary programmingcan be used to generate and to optimize spatial filter locations and coefficient values for thepurpose of classifying targets using intra-region pixel relationships [48]. In other words,the evolutionary programming algorithm can optimize the locations and coefficient valuesto approach the largest target-clutter separation distance.14.4.4.7 Hybrid (Image Library and Model)A typical template library is quite large and must be coarsely sampled to span the entire hypothesisspace. One method of reducing the total number of templates required for handlingvarious collection geometries, obscuration, and articulation conditions and increasing thesampling resolution of the hypothesis space is to use a hybrid model/template approach.The library size can be reduced by using a model to determine the transformation or perturbationsnecessary to adapt an existing template to the signature of a neighboring sensorgeometry or target articulation, while maintaining some of the computational advantage ofthe pure template-based approach [42]. An alternative hybrid approach for increasing theresolution over the hypothesis space can be constructed by using the templates to reducethe number of hypotheses that the models must search over; however, a large template setis still required for this approach [42].14.4.4.8 Mean-Squared Error (MSE)The first algorithm to be discussed for classifying targets, the mean-squared error algorithm,is a template-based approach [49]. This algorithm compares the image under testwith a pre-defined, normalized, and windowed set of template images for target types ofinterest. The normalization process should remove the clutter background level to eliminateerrors from absolute RCS calibration of the sensor data, while windowing eliminatesthe influence of nearby clutter in the classifier. The MSE is calculated over the pixels inthe reference template window to calculate the relative difference in total power. If theMSE between the test image and the templates for the given target types always exceedsa maximum allowable threshold, then the detected object is defined as clutter; otherwise,the object is classified under the target type corresponding to the lowest MSE [1]. Withthis approach, the number of target classes can be doubled with only a slight decrease inATR performance [27].14.4.4.9 Distances Between FeaturesThe Hausdorff distance is tolerant of small position errors [50] and is invariant to rotationgiven scale and signal level normalizations have been performed [51]. The directedHausdorff distance finds the point in the first set that is farthest from any point in the