检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:邓小炼[1] 王长耀[1] 王汶[1] 张庆员[1] 李向军[1]
出 处:《国土资源遥感》2005年第2期7-11,共5页Remote Sensing for Land & Resources
基 金:国家高技术研究发展计划(863计划2003AA131170);中国科学院知识创新工程重大项目(KZCX1-SW-01-02)资助。
摘 要:鉴于影像灰度控制点匹配算法运算量大、识别精度低以及约束条件多等不足,本文对该算法做了改进。主要思路是:在进行模板运算时,对目标影像采用动态模板进行不等距搜索;利用灰度相关系数双阈值和等角变换,对目标控制点进行判别;结合控制点间的空间位置关系,对未识别出的控制点进行定位。文中给出了具体的实施流程,并采用ASTER和TM两种成像差异显著的图像数据,对优化前后的匹配算法进行对比试验。结果表明,改进算法在运算效率、识别精度以及适应性方面,都比传统算法有明显优势。There exist some shortages in the traditional image matching algorithm based on gray degree, such as huge quantities of calculation, relatively low accuracy, and too many restrictions in application. In order to solve these problems, this paper puts forward an optimized remote sensing image matching algorithm. The main ideas include the following several aspects: On the basis of confirming the subimage of the target image by understanding prior knowledge of remote sensing image, the first step is to search the subimage of the target image non-equidistantly with dynamic template, the second step is to locate the target position by two threshold gray degree correlation coefficients and conformal transform, and the last step is to judge the target position of the ground control point not recognized correctly by the spatial location relations of ground control points. The work flow is introduced in detail. Moreover, a comparison experiment on traditional and modified image matching algorithms is performed with an ASTER image and a TM image. From the results obtained, we can reach the conclusion that the modified algorithm is superior to the traditional algorithm in that it has much more higher accuracy and efficiency than the latter and hence it should have higher adaptability and applicability.
分 类 号:TP75[自动化与计算机技术—检测技术与自动化装置]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.3