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

Abstract

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.

Keywords

• synthetic aperture radar
• radar imaging
• feature extraction
• gabor filters
• image classification
• image filtering
• nearest neighbour methods
• principal component analysis
• synthetic aperture radar image target classification
• remote-sensing image application
• k-nn classifier
• k-nearest neighbour classifier
• multiscale gabor feature extraction
• multitypes sar image target classification method
• principle component analysis
• mstar database