基于形状先验的遥感目标可信识别技术研究

项目名称： 基于形状先验的遥感目标可信识别技术研究

项目编号： No.61202199

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 计算机科学学科

项目作者： 孔丁科

作者单位： 浙江工商大学

项目金额： 24万元

中文摘要： 随着遥感技术的迅速发展，图像分辨率越来越高，对遥感图像处理和应用研究提出了新的挑战。遥感目标的精确提取与识别不仅依据光谱或者强度信息，对空间关系以及目标形状等特征信息和先验知识也存在较强的依赖性。基于形状信息的遥感目标识别技术能根据形状约束指导目标提取与识别，增强了遮挡、阴影等信息缺失情况下遥感目标识别的鲁棒性，更切合当前遥感图像的实际应用需求。本项目旨在研究基于形状先验的遥感目标可信识别技术，探求实现遥感目标可信识别的形状先验水平集模型：结合非参数建模理论，建立形状水平集建模中形状描述以及一致性表达等问题的一体化解决方法；明确全局形状约束和局部结构表征的关系，建立实现目标精确提取的局部拟合形状先验模型；优化先验形状的量化表征，建立用于遥感目标自动识别的多特征融合变分水平集模型框架。本项目研究将丰富遥感图像目标识别的理论与方法，为实现高效率、高精度的遥感目标自动识别提供技术支持。

中文关键词： 遥感图像；水平集；形状先验；目标识别；

英文摘要： With the rapid development of remote sensing technology, high resolution has become available in current series of remote sensing images. The remote sensing image interpretation presents new challenges. Actually, object recognition for remote sensing imagery is not only based on the spectral or intensity information, but also has a strong dependence on spatial relationships, shape information and prior knowledge. Object recognition technologies using shape information can efficiently guide the object extraction and recognition according to the shape constraint, thus enhance the robustness of handling remote sensing images with information-absent, such as shade and shadow. Therefore, they are more suitable to actual requirements of remote sensing imagery.The aim of this project is to seek shape prior based reliable object recognition for remote sensing imagery via variational models. Firstly, based on the nonparametric modeling theory, we build an integration solution including shape description and consistent expression via level set. Secondly, we investigate the relationship between global shape constraints and local structural characterization, and realize the shape constrained local fitting models for accurate extraction. Then, we establish a variational level set framework for automatic target recognition of

英文关键词： Remote Sensing Imagery；Level Set；Shape Prior；Object Recognition；