Principles of Modern Radar - Volume 2 1891121537

14.4 Synthetic Aperture Radar 641Texture Features Common textural features used for discrimination of man-made targetsfrom natural clutter include: standard deviation, fractal dimension, and weighted fill [1].Standard deviation is simply the standard deviation of the data within a target-sized templateto measure the fluctuation of pixel intensities, which are generally larger for targetsthan natural clutter [12,19]. Fractal dimension measures the spatial distribution of thebrightest scatterers in the detected object and can be calculated asdim(S) = limn→0log M nlog 1 n(14.11)where M n is the minimum number of n × n boxes needed to cover the detected object,S [12]. The weighted-rank fill ratio measures the fraction of the total power present inthe brightest 5% of the detected object scatterers, which is higher for targets than naturalclutter [12].Contrast Features Common contrast features used for rejection of objects without asufficient local brightness include: peak CFAR, mean CFAR, and percent bright CFAR[1]. To be more specific, peak CFAR is the maximum value within the target-shaped blobin the CFAR image created by the algorithm described in 14.4.3.3.1 [1]. The mean CFARfeature is the average CFAR over the target shaped blob, and the percent bright CFAR isthe percentage of pixels within the target-shaped blob that exceed a threshold set basedon the training data [1].Size Features Common size features used for rejection of inappropriately sized objectscompared with the targets of interest include: mass, diameter, and normalized rotationalinertia [1]. The Radon transform can be used to find the length and width of a target. Firstthe target chips from the pre-screener are segmented into background, shadow, and targetclasses using two-parameter CFAR, morphological processing, and feature clustering. The2D Radon transform is then applied to the binary image of just the target. The height, ormaximum Radon transform value, for each projection angle is found. The width (W ) isfound as the projection angle with the minimum height, while the diagonal (D) has themaximum height. The length is √ D 2 − W 2 [19].Additional Features To reduce false alarms from building returns, features from differenttypes of SAR imagery can be exploited to identify and ignore buildings. In single strip-mapSAR images, cardinal streaks and supporting shadows can be used to identify buildings.Multi-pass airborne SAR images can be registered to extract 3-D heights and linear patternsassociated with buildings. Finally, local histogram thresholding can be used to segmentbuildings from the ground in interferometric SAR images [20].Most ATR performed on SAR images is done to identify stationary targets; however,some features can be extracted for classifying moving targets. For instance, high rangeresolution (HRR) profiles can be extracted from SAR images as motion-invariant featuresfor identifying moving ground targets; HRR profiles formed in this way have a highertarget-to-clutter signal ratio than those formed from raw data because much of the groundclutter is removed during Doppler processing in the process of forming a SAR image [21].Feature Dependency on Angle Because the aspect angle to an unknown target is likelyunknown itself, the discrimination phase of ATR may begin with an algorithm to estimate