Abstract
Automatic target recognition (ATR) is the task of classifying sensed imagery from synthetic aperture radar (SAR) automatically into a canonical set of target classes. Here, a method to recognise different classes of military vehicles based on the combination of quaternionic wavelet transform (QWT) and principal component analysis (PCA) features is presented. To identify the certain region of SAR images, patches are extracted over the interest points detected from the SAR images. Then QWT features and PCA features are computed and combined for every patch. These extracted features are trained and classified using SVM. The performance of QWT is compared with two more multiresolution transforms such as ridgelet transform and log Gabor transform as well as the Scale and rotation-invariant interest point detector and descriptor, named speeded up robust features (SURF). Observations revealed that QWT outperforms the ridgelet transform, log-Gabor and SURF. The experimental evaluation is done using the MSTAR database.
Published Version
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