农用地遥感影像信息的角提取方法

在高分辨率影像中，耕地和坑塘具有显著的角特征点，该文根据此特点准确地实现这两类地物信息的提取。在角提取时，针对TK（Tomasi-Kanade）角检测法对角方位的变化较为敏感这一缺陷，结合双向分析技术对传统的角检测算法加以改进，并提出鲁棒性更高的双向TK角检测；利用COVPEX（corner validation based on corner property extraction）算法对角检测结果进行验证，发现验证结果中不仅存在一些对于影像分析来说几乎无利用价值的伪角，而且还存在“角簇”现象，前者降低了验证结果的合理性，而后者破坏了角的唯一性。针对这两个问题，该文联合多尺度分析技术对COVPEX算法进行改进，提出了多尺度COVPEX算法和“去角簇”操作，角提取的精度和合理性均得到了明显地提高。

Corner extraction algorithm for high-resolution imagery of agricultural land

Based on the characteristics that the farmlands and ponds have significant comers in high-resolution image, the extraction of farmlands and ponds is achieved accurately in this paper. During the corner extraction procedure, according to the sensitivity of the corner direction change, traditional algorithm was improved with bidirectional analysis technology and more robust bidirectional TK (Tomasi-Kanade) corner detector was proposed. COVPEX (Corner validation based on corner property extraction) was used to validate result of corner detection. Authors found not only the pseudo-corners which were almost useless for image analysis but also the corner-clusters were presented in the validation result. The pseudo-corners reduce rationality of the results, and the corner-clusters destroy the uniqueness of corner. To solve the problems, COVPEX algorithm was improved by associating with multi-scale analysis technology, multi-scale COVPEX algorithm and the "removal comer-cluster" operation were proposed. The results showed that the accuracy and rationality of corner extraction were significantly improved.