优化选择的融合纹理—光谱—空间相关性多特征一体化的高光谱图像分类方法研究

项目名称： 优化选择的融合纹理—光谱—空间相关性多特征一体化的高光谱图像分类方法研究

项目编号： No.41301383

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： 李山山

作者单位： 中国科学院遥感与数字地球研究所

项目金额： 25万元

中文摘要： 高光谱遥感能够提供丰富的地物波谱信息，利用高光谱图像进行地物目标识别和分类具有重要意义。仅利用光谱特征分类难以解决光谱异质性造成的"同物异谱"等问题，将纹理特征或空间相关性特征引入高光谱图像分类中，已成为近年来研究的热点，但仍存在如下问题：（1）单一纹理特征提取方法已难以满足不同地物类型复杂纹理提取的需求；（2）缺乏能够同时将纹理特征、光谱及空间相关性特征等多特征整合的分类模型；（3）忽视了地物边界等细节结构信息的保持。针对以上问题，本项目将以纹理-光谱-空间相关性多特征一体化为核心研究高光谱图像精细分类。首先，基于不同纹理提取算法结果，展开基于智能化蚁群算法的多纹理特征融合算法研究；其次，提出基于扩展马尔科夫随机场的纹理-光谱-空间相关性多特征一体化分类判别模型。通过本项研究，实现高光谱图像精细分类，在提高整体分类精度的同时保持地物边界细节信息

中文关键词： 高光谱遥感；监督分类；特征提取；支持向量机；

英文摘要： Hyperspectral imagers can proivide detailed spectral information of various ground cover types due to its wide coverage of wavelength and high sampling rate.It is very important to make use of hyperpspectral data for target recognition and landscapes classification. However,it is difficult to deal with higher spectral variance within each class corresponding to land-cover units only using spectral features.The improtance of integration of texture, spatial contexture and spectral patterns simultaneously has been identified as a desired goal by many scientists devoted to hyperspectral data analysis.However, there exits three issures in spatial-spectral classification:(1)Individual texture extraction method can not handle reqirement of complex texture.(2)It is very lack of classificaiton discriminant model which can integrate texture, spectal and contextrual features simultaneously.(3)Class boundary and details of structure information has been ignored easily in classification process.Address these issues, a method that integrates texture, spectral and spatial contextual features for improved hyperspectral classification is presents.At the beginning, proper fusion of texture features derived from different texture method is expected to produce an improved feature set based on Intelligent ant colony optimal (ACO) al

英文关键词： Hyperspectral remote sensing；supervised classification；feature extraction；support vector machine；