高斯过程模型在SAR图像目标识别中的应用

将高斯过程模型应用于合成孔径雷达（SAR）图像目标识别。高斯过程模型是基于贝叶斯框架的统计学习算法，通过结合核函数和和概率判别构建分类模型。与传统分类模型相比，高斯过程模型可以获得更高的分类效率和精度。方法实施过程中，采用SAR图像的特征矢量作为输入，以目标类别标签作为输出训练高斯过程模型。对于待识别样本，通过计算其在高斯过程模型下属于各个类别的后验概率判定其目标类别。实验中，依托MSTAR数据集在典型条件下开展测试。根据实验结果，所提方法在标准操作条件下对10类目标识别精度达到99.28%；在30°和45°俯仰角下的平均识别率分别为98.04%和73.13%；在噪声干扰各个信噪比条件下均保持最高性能。实验结果验证了所提方法的有效性和稳健性。

Application of Gaussian process model in SAR image target recognition

The Gaussian process model was applied to synthetic aperture radar (SAR) image target recognition. Gaussian process model was a statistical learning algorithm based on the Bayesian framework, which combines the kernel function and probability judgement to build the classification model. Compared with the traditional classification models, the Gaussian process model could achieve higher classification accuracy and precision. In the implementation of target recognition, the feature vectors from SAR images were used as the inputs while the target labels were employed as the outputs thus training the Gaussian process model. For the test sample to be classified, the posterior probabilities related to different classes were calculated thus determining its target label. In the experiments, typical situations were set up to test the proposed method using the MSTAR dataset. According to the experimental results, the proposed method could achieve 99.28% recognition accuracy for 10 types of targets under standard operating conditions. The average recognition rates at 30° and 45° depression angles were 98.04% and 73.13%, respectively. Under noise corruption, the best performance was achieved by the proposed method at each noise level. The results validated the effectiveness and robustness of the proposed method.