像元与对象协同的遥感影像多语义尺度统计分割研究

项目名称： 像元与对象协同的遥感影像多语义尺度统计分割研究

项目编号： No.41301470

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 天文学、地球科学

项目作者： 郑晨

作者单位： 河南大学

项目金额： 25万元

中文摘要： 在高空间分辨率遥感影像信息提取中，基于像元和面向对象的分析方法往往被分开研究，像元级方法难以考虑对象级特征，对象级方法中初始区域误分需要像元层面修正，两者之间缺乏有效的协同。本课题以探索基于语义信息的多尺度构建方法为基础，研究像元级与对象级协同的统计多尺度分割算法。课题首先根据影像的语义层次信息，研究基于区域高低频分解的多尺度理论和构建方法；然后，建立多尺度结构下的区域邻接图，研究马尔科夫随机场（MRF）模型对图中对象级层面的区域和像元级层面的边界协同建模的方式；最后，研究MRF模型迭代求解过程中像元与对象间的相互作用机理，探索区域标记结果自上而下对像元边界的反馈机制和像元边界调整自下而上对区域误分的修正方法，在贝叶斯理论框架下求解模型结果，并设计实用的分割算法。研究像元与对象协同的多尺度MRF建模求解分割算法，为高分辨率遥感影像自动解译提供了一条新的思路，具有重要的理论价值和应用价值。

中文关键词： 高分辨率遥感影像；马尔科夫随机场；多尺度；语义；分割

英文摘要： For extracting information from the high spatial resolution remote sensing image, pixel-based methods and object-based methods are always separate studying, pixel-based methods hardly consider the object features, the misclassifications of initial regions in the object-based methods need to revise at the pixel level, these two type methods neglect the connection between them. This study is based on the multiresolution representation cooperating with the semantics, uses the statistics model to combine the benefits of both the pixel-based and object-based methods. First, the multiresolution theory and the representation way for hierarchical semantics is researched by decomposing the image into the low frequency and the high frequency based regions. Then, building the region adjacent graphic under the region multiresolution structure, study how to use the Markov random field (MRF) model to synergistically model the object-level regions and the pixel-level boundaries in the graphic. At last, learn how the pixels and objects interact during the iterations of solving the model, research the feedback from the region labels to boundaries and the revision from the boundaries to the misclassifications of regions, obtain the result based the Bayesian framework, design the useful segmentation algorithm. The method cooperati

英文关键词： high resolution remote sensing image；Markov random field；multiresolution；semantic；segmentation