SAR image classification method based on Gabor feature and K-NN

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.