面向光谱-空间特征集合的高光谱遥感影像度量学习与分类研究

项目名称： 面向光谱-空间特征集合的高光谱遥感影像度量学习与分类研究

项目编号： No.41501392

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 彭江涛

作者单位： 湖北大学

项目金额： 20万元

中文摘要： 分类是高光谱遥感图像处理领域的研究热点。传统的基于光谱像素点的分类方法，因未考虑空间邻域像素的相关性，而效果欠佳。结合空间域和光谱域信息（空谱信息），进行高光谱图像分析，是提升分类性能的有效途径。其中，关键问题在于如何有效地利用空谱信息，建立合理的光谱域和空间域相似性度量。为此，本项目将光谱像素点与其空域邻域像素相结合，以高光谱图像局部同源区域或像素集为对象，建立区域或集合相似性度量，以期实现准确的地物目标分类。项目研究内容如下：1）集合凸包距离度量学习与分类，具体包括像素集结构相似性描述、正则化集合度量学习、像素集稀疏表示、核表示及协同表示分类；2）集合核度量学习与分类，包括局部同源区域的多尺度表示框架、集合核定义、集合核理想正则化提升算法、空谱组合集合核分类框架。项目研究成果有助于提升高光谱图像分类精度、推进高光谱遥感信息处理水平，具有重要的学术价值和较大的应用前景。

中文关键词： 高光谱遥感；支持向量机；机器学习；核方法；特征提取

英文摘要： Classification is an important and hot research issue in the field of hyperspectral remote sensing image processing. The traditional spectral-based classification methods perform poor mainly because they don’t exploit the spatial information and haven’t considered the inter-pixel correlations. Combining the spatial and spectral information for hyperspectral image (HSI) processing is an efficient way to improve the classification performance and becomes the development trend of hyperspectral analysis, where the use of spatial information and construction of spatial and spectral similarity metrics are crucial. Therefore, by combining the spectral pixel and its spatial neighboring pixels, the project builds set-to-set similarity metrics for the HSI classification based on the local homogeneous pixel sets of HSI, which aims to obtain desirable spectral-spatial classifiers. Based on the spectrally point-to-point similarity metric, the project focuses on: 1) convex-hull-based set-to-set similarity metric learning and classification model, including the description of the set-to-set structural similarity, regularized set-to-set distance metric learning, set-based sparse representation classification, kernel classification and collaborative representation classification algorithms; 2) set-to-set kernel similarity metric learning and classification model, including the multi-scale representation of local homogeneous regions, definition of set-to-set kernel, ideal regularization for the set-to-set kernels, composite spatial and spectral set-to-set kernels framework. The scientific research achievements help to improve the accuracy of HSI classification and to promote the remote sensing information processing level, which have important academic value and large application prospect.

英文关键词： hyperspectral remote sensing;support vector machine;machine learning; kernel-based method;feature extraction