用于自动识别遥感图像路网信息的改进模糊连接度方法

郑瑾; 柳肃; 孙炜

doi:10.11999/JEIT150563

用于自动识别遥感图像路网信息的改进模糊连接度方法

doi: 10.11999/JEIT150563

郑瑾¹,
柳肃¹,
孙炜²

1.
(湖南大学建筑学院长沙 410082) ②(湖南大学电气与信息工程学院长沙 410082)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-05-12
- 修回日期: 2015-10-19
- 刊出日期: 2016-02-19

An Improved Fuzzy Connectedness Method to Recognize Automatically the Road Network Information from Remote Sensing Image

1.
(College of Architecture, Hunan University, Changsha 410082, China)
2.
(College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

摘要

摘要: 针对遥感图像中路网信息的自动识别问题，该文将小波模极大值边缘检测方法和模糊连接度方法结合，提出一种改进的模糊连接度方法。采用小波模极大值边缘检测方法进行模糊连接度种子点的自动选择，解决传统模糊连接度理论中种子点难以自动选择的问题。在此基础上，对传统的模糊相似度计算公式进行简化，在保证路网识别准确性的同时，大大减少了计算量。采用来自Quickbird高分辨商业遥感卫星的3组影像进行实验，验证了该文提出的路网识别方法具有较高的准确性和计算速度。
- 遥感图像 /
- 路网信息识别 /
- 模糊连接度 /
- 小波模极大值 /
- 图像边缘检测
Abstract: To recognize automatically road network from remote sensing image, an improved fuzzy connectedness method is proposed by combining traditional fuzzy connectedness theory with wavelet modulus maximum algorithm. The wavelet modulus maximum image edge detection algorithm is used to solve the problem of selecting seed points automatically in traditional fuzzy connectedness theory. On this basis, traditional fuzzy similarity computational formula is simplified. This can reduce the cost of calculation greatly without reducing the recognition accuracy. Three high-resolution remote sensing images from the satellite Quickbird are processed in the experiments to prove the effectiveness of the proposed method. The results show that the proposed road network recognition method has high accuracy and rapid computation speed.
- Remote sensing image /
- Road network information recognition /
- Fuzzy connectedness /
- Wavelet modulus maximum /
- Image edge detection

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参考文献(25)

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