中文摘要：

采用与传统的利用特征匹配方法进行地物目标识别不同的思路，提出一种基于显著语义模型的机场与油库目标的识别方法．该方法在低层特征空间利用视觉关注模型将航拍图像分解成若干个视觉显著性子图，提取出目标可能存在的候选区域；对训练图像集构建基于SIFT局部特征的特征袋语义模型，并利用模型中的特征字典提取出显著性子图所包含的显著语义特征，以实现对机场和油库目标的快速检测识别．利用GoogleEarth构建了多种不同成像条件下的典型目标数据库，对文中方法的有效性进行验证．实验的结果表明，该方法比传统的特征匹配方法具有更好的识别性能和更高的运算效率，同时对于光照、视点和尺度变化等干扰具有较强的鲁棒性．

英文摘要：

Different from the conventional ground o technique, an airport and oil depot recognition method bje is ct recognition based on feature matching presented in this paper, which is based on salient semantics model. Specifically, the proposed method utilizes the visual attention model to decompose the aerial image into several visual salient subgraphs in low-level feature space, which are the candidate regions object may exist. Meanwhile the training images are applied to construct the bag- of-features （BoF） semantics model via SIFT local features, and the salient semantic features of the subgraphs can be extracted with feature dictionary of BoF model. Con airport and oil depot can be quickly implemented. Multiple typical grou seq nd uently the recognition for object database is used to test, which is acquired under different imaging conditions from Google Earth. Experiments on the database demonstrate the proposed method has better recognition performance and higher efficiency compared with traditional feature matching methods, and also more robust to the influence of illumination, viewpoints and scale.