基于蚁群算法面向对象的遥感图像分类方法研究

项目名称： 基于蚁群算法面向对象的遥感图像分类方法研究

项目编号： No.41301371

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： 叶志伟

作者单位： 湖北工业大学

项目金额： 25万元

中文摘要： 遥感图像分类是遥感信息分析与应用中最基本的问题之一。传统基于像素的分类方法难以克服分类过程中未利用空间信息的局限性。面向对象的分类技术利用了空间信息，弥补了传统方法的不足。然而由于遥感图像的多尺度性和复杂性，目前并没有鲁棒的遥感图像多尺度分割算法；其次，由于面向对象的分类方法用到了高维特征，会导致维数灾问题，且单一分类器在处理这些特征时常达不到理想的分类效果。 本项目提出基于蚁群算法和面向对象技术，并融合多分类器系统解决上述问题。首先利用谱图理论对遥感图像进行多尺度分割，并在最优尺度分割图像上提取基于光谱、纹理、形状等多种子图像特征；然后利用蚁群算法融合遗传算法对各成员分类器进行最优特征子集选择；最后，利用蚁群算法融合遗传算法的协同进化新方法对多分类器融合集成学习问题进行优化求解；在已获得各子分类器最优特征子集的基础上，完成多分类器系统的构建，从而有效地提高面向对象遥感图像分类方法的精度。

中文关键词： 优化；遥感图像分类；特征提取；特征选择；集成学习

英文摘要： Remote sensing image classification is one of the most fundamental problems in area of remote sensing information analysis and application. The traditional pixel-based classification methods, which utilize only spectral information, can not overcome the main limitation which does not make use of space information in the process of classification.In contrast,object-oriented classification techniques of remote sensing image, integrating information of spectrum, shape and texture of image, utilize spatial information so that to overcome the drawback of the traditional methods while dealing with classification of remote sensing image. However,currently, there is not robust multiscale segmentation algorithm for remote sensing image because of its property of multiscale and complex . Moreover,a single classifier often can not achieve a stably ideal result while dealing with those features beacase object-oriented classification needs to use a variety of high-dimensional features with significant differences in nature and high-dimensional features will lead to curse of dimensionality. Hence,this reseach proposes to apply object-oriented technique fused with multiple classifier based on ant colony optimiaztion algorithm to tackle the problems mentioned above.The main idea of this research is illustred as below. First,the

英文关键词： Optimization；Remote sensing image classification；Feature extraction；Feature selection；Ensemble learning