基于条件随机场模型和森林三维形态结构的树种分类算法研究

项目名称： 基于条件随机场模型和森林三维形态结构的树种分类算法研究

项目编号： No.41201446

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 地理学

项目作者： 王瑞瑞

作者单位： 北京林业大学

项目金额： 25万元

中文摘要： 智能分析理论以鲜明的面向对象的特点成为森林资源综合防控与监测领域的热门技术。目前常用的智能分类识别模型一方面大都假设观测数据是独立同分布或条件独立的，不符合森林树种的分布规律；另一方面在模型训练过程中无法加入目标的其他特征信息，无法充分利用多平台多维影像的特征。另外，森林地区通常具有一定的地形起伏，分布复杂多变，其分类识别与空间形态结构信息密不可分，而传统空间信息获取的方法存在数据源较为单一和成本相对较高的缺陷，对形态结构信息的利用多限于二维。本项目以森林树种的智能分类识别模型为研究对象，围绕树种智能识别开展算法与模型的创新性研究，重点研究如何获取树木三维形态结构信息，改进可以融合上下文信息和多特征参与训练的条件随机场判别式模型，对森林地区的树种进行高精度智能分类识别。项目研究内容具有明确的应用目标和迫切的需求背景，研究成果将有利于我国林业发展的信息化。

中文关键词： 树种智能识别；条件随机场；三维形态结构信息；树木形态参数提取；显著性区域分割

英文摘要： The theory of intelligent analysis with the oriented feature has become the hot technology of forest reserves survey and protection fields. On one side, the traditional intelligent recognition models commonly suppose that the distribution of the observation data is independent identically or independent conditionally. These hypothetical conditions do not confirm the regularity of tree species distribution. On the other hand, the other features of object cannot be put into the process of model training, which cannot take full advantage of the multi-platform multi-dimensional images. Moreover, due to the topographical relief and the complex distribution of forest region, the tree species recognition is tied to spatial information. However, the data sources of some traditionally spatial information acquisition methods are commonly limited to the homologous images with the same scale or the high cost. In this project, the innovational research is developed on the intelligent recognition model and algorithm of tree species. At first, the stereo morphological model is developed, and then the three dimension morphological structures are generated based on this model; at last, the conditional random fields (CRFs) is improved for the tree species recognition. The distinct character of the CRFs is to fuse the up and down

英文关键词： Intelligent recognition of tree species；Conditional Random Fields；three dimension morphological structure；extraction of tree species morphological parameter；salience region segmentation