A method for Synthetic Aperture Radar （SAR） image target recognition based on Principal Component Analysis （PCA）

Independent Component Analysis （ICA） and Gabor wavelet decision fusion is presented in this paper. PCA

ICA and Gabor wavelet transformation were used to extract feature vectors from SAR target images

respectively. Three Support Vector Machine （SVM） classifiers were applied to classify the feature vectors extracted via three algorithms

respectively. Ranking based decision fusion algorithm was then used to fuse the outputs of three classifiers. The final classification decision result was obtained from the output of the fuser. Experiments were implemented with three military targets in MSTAR database. The experimental results show that the probability of correct classification obtained by PCA

ICA and Gabor wavelet decision fusion is better than that attained by any of the individual feature. Therefore

it is concluded that the method proposed in this paper advances the probability of correct classification and can be an effective approach for SAR image target recognition.