SAR target recognition using PCA, ICA andGabor wavelet decision fusion
PCA、ICA和Gabor小波决策融合的SAR目标识别

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 Gaborwavelet 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 decisionfusion algorithm was then used to fuse the outputs of three classifiers. The final classification decision result was obtained fromthe output of the fuser. Experiments were implemented with three military targets in MSTAR database. The experimental resultsshow that the probability of correct classification obtained by PCA, ICA and Gabor wavelet decision fusion is better than that attainedby any of the individual feature. Therefore, it is concluded that the method proposed in this paper advances the probabilityof correct classification and can be an effective approach for SAR image target recognition.