【摘要】 提出一种基于无样本的SAR图像目标检测分类方法。针对空战过程中难以获得大量SAR图像目标样本问题,采用基于全局CFAR的多尺度SIFT特征进行目标纹理描述,并针对特征维度较高及特征描述之间存在的冗余问题,通过PCA算法对其进行降维处理后,采用DBSCAN算法对潜在目标区域的多尺度SIFT特征进行分类实现目标检测。通过单一目标和多类目标图像进行实验验证,实验结果表明该方法具有一定的有效性和可行性。更多还原

【Abstract】 A sample-free SAR image target detect classification method is proposed in this thesis.Aiming at the problem that it’s difficult to obtain plenty of SAR image objective samples in the process of air combat,the multi-scale SIFT feature based on global CFAR is adopted to describe the objective texture,and the dimensionality reduction processing is applied by PCA algorithm for the problem of higher feature dimensions and the redundancy between feature descriptions.DBSCAN algorithm is used to classify the multi-scale SIFT features of the potential target region to achieve the objective detection in final. The single target and multiple objective images are used to verify the feasibility and effectiveness of the proposed method in the end.更多还原