基于非规则图形标识点过程的高分辨率遥感影像几何特征提取方法研究

项目名称： 基于非规则图形标识点过程的高分辨率遥感影像几何特征提取方法研究

项目编号： No.41271435

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 天文学、地球科学

项目作者： 李玉

作者单位： 辽宁工程技术大学

项目金额： 75万元

中文摘要： 高空间分辨率遥感影像提供地球表面丰富的几何信息，从而为精确提取其中的几何特征提供了充分的依据和可能。同时，高空间分辨率也使得遥感影像中同一地物目标内像素光谱测度的相似性减弱，不同地物目标间像素光谱测度的差异性减弱，以及地物目标内几何噪声增大，由此增加了几何特征提取的难度。为了解决上述矛盾，本项目针对高空间分辨率遥感影像几何特征提取问题开展系统的理论与实践研究。立足于随机几何中标识点过程的理论与方法，重点研究以非规则多边形为标识的标识点过程的构建及特性、地物目标几何形状的非规则多边形拟合、几何特征融入图像建模等问题。在此基础上建立基于地物目标本身而非像素的几何特征提取模型，为开发具有广泛适用性的基于标识点过程的高空间分辨率遥感影像几何特征提取算法奠定坚实基础。研究成果将在基于高空间分辨率遥感影像的大规模土地利用/覆盖分类、城市目标提取、灾害评估、环境监测等方面发挥作用。

中文关键词： 标识点过程；特征提取；非规则几何；遥感影像；高分辨率

英文摘要： Remote sensing images acquired with the last generation high spatial resolution sensors provide great geometric precision and a high level of thematic detail of objects on the Earth surface. The significant amount of geometric details presented in a fine scene makes it possible to extract geometric features hidden in these high spatial resolution images. But at the same time, the improvement in spatial resolution increases the internal spectral variability of each land cover class, decreases the spectral variability between different classes, and induces geometric noise through the land cover caused by tiny targets on it. Thus, the resulting defects from high spatial resolution lead to the difficulty in the extraction of the geometric features. Accordingly, the project will address the development of novel techniques for solving the problem. Stochastic models based on marked point processes have proven to be powerful tools to deal with geometric feature extraction problems from high spatial resolution remote sensing images, and have already led to convincing experimental results in various applications such as extraction of buildings, road networks, and tree crowns. The marked point processes exploit random variables whose realizations are configurations of geometric objects, e.g., rectangles, segments, or ellip

英文关键词： Marked Point Process；Feature Extraction；Irregular Geometry；Remote Sensing Imagery；High Resolution Image