高光谱图像混合像元处理技术研究

项目名称： 高光谱图像混合像元处理技术研究

项目编号： No.61275010

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 无线电电子学、电信技术

项目作者： 王立国

作者单位： 哈尔滨工程大学

项目金额： 75万元

中文摘要： 针对目前高光谱混合像元处理技术的发展现状和迫切需求，课题拟研究端元选择、光谱解混和亚像元定位及彩色显示等热点技术。主流端元选择方法包含大量体积计算，且需降维预处理；传统基于线性光谱混合模型的解混方法无法准确刻画类内光谱变化，而新兴的支持向量机解混模型与解混要求不完全一致；当前亚像元定位模型中空间相关性未能充分贯彻，而同时考虑光谱和空间约束的马尔可夫随机场模型约束条件单一；现有的典型可视化方法建立在低精度的分类结果之上。本课题通过研究新型距离测算方法建立免于降维预处理与体积计算的快速端元选择算法；在支持向量机解混模型中施加解混误差约束来提高光谱解混性能；建立完全贯彻空间相关性的新空间引力模型及多约束马尔可夫随机场模型以获得高精度的亚像元定位结果；利用光谱解混与亚像元定位的形数结合信息实现高精度多层次的可视化技术。本课题的成功研究对高光谱图像信息的有效挖掘和利用有着重要的理论意义和应用价值。

中文关键词： 高光谱；端元选择；光谱解混；亚像元定位；彩色显示

英文摘要： Due to the current development trend and urgent requirement of mixed pixel processing techniques on hyperspectral imagery, in this project researches will be carried out on endmember extraction, spectral unmixing, sub-pixel mapping and color display for hyperspectral imagery. Some classical endmember extraction algorithms require massive calculation of volume as well as dimension reduction. The traditional linear spectral mixing models based spectral unmixing approach fails to accurately describe the intra-class spectral variation, and the cost function of the newly developed support vector machine based unmixing model is not consistent with the ultimate goal of spectral unmixing. In current sub-pixel mapping methods, the description of spatial dependence is quite rough, and the effectiveness of Markov random field based sub-pixel mapping model which has the advantage of simultaneously considering spatial and spectral constraints is limited by the single constraint. The typical color display approaches are processed based on the lowly accurate classification results. The goal of this project is to look for effective methods to solve these issues. Specifically, a fast endmember extraction algorithm will be developed which will transform the volume calculation to the distance calculation and more importantly, no d

英文关键词： hyperspectral；endmember selection；spectral unmixing；subpixel mapping；colour display