Automatic target recognition in SAR images using quaternion wavelet transform and principal component analysis Online publication date: Fri, 31-Mar-2017
by S. Arivazhagan; R. Ahila Priyadharshini; L. Sangeetha
International Journal of Computational Vision and Robotics (IJCVR), Vol. 7, No. 3, 2017
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.
Online publication date: Fri, 31-Mar-2017
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org