Open AccessSAR image classification method based on Gabor feature and K‐NN
Author(s) -
Wang Zhiru,
Chen Liang,
Shi Hao,
Qi Baogui,
Wang Guanqun
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0382
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , classifier (uml) , synthetic aperture radar , feature extraction , contextual image classification , computer vision , gabor transform , image (mathematics) , time–frequency analysis , filter (signal processing)
Synthetic aperture radar (SAR) image target classification is a hot issue in remote‐sensing image application. Fast and accurate target classification is important in both military and civilian fields. Consequently, this study proposes a novel SAR image target classification method based on Gabor feature extraction and K‐NN classifier. First, the multi‐scale Gabor features of SAR image are extracted. Then, a k‐nearest neighbour (k‐NN) classifier with principle component analysis is trained by the extracted Gabor features. Finally, the classifier is used to realise the multi‐types SAR image targets classification. MSTAR database is used to validate the classification ability. Experimental results demonstrate that the proposed method has superior performance in term of efficiency and accuracy.