基于形态学空间特征的高光谱遥感图像分类方法  

Classification of hyperspectral remotely sensed images based on the extraction of spatial feature

作　　者：吕俊伟[1] 樊利恒 石晓航[1] 

机构地区：[1]海军航空工程学院控制工程系,山东烟台264001 [2]海军航空装备计量监修中心,上海200436

出　　处：《光学技术》2016年第5期385-391,共7页Optical Technique

基　　金：国家自然科学基金资助项目(61032001;60801049);国家高技术研究发展计划创新基金资助项目(2010AAJ140)

摘　　要：传统的高光谱图像分类主要是基于像素的光谱特征,在一定程度上忽略了高光谱遥感图像中像素之间的空间相关性。为了充分利用高光谱图像中的空间信息,提出了一种基于加权多结构元素无偏差形态学的空间特征提取方法,并基于形态学的多尺度特征和结构保持性提出了基于邻域的多尺度空间特征提取方法,得到了高光谱遥感图像的空间特征。对k-NN分类算法进行改进,提出了基于变精度粗糙集和重构误差的k-NN分类算法,实现了基于空间特征的高光谱遥感图像分类。在两个不同的高光谱遥感图像的实验验证了基于空间特征和改进k-NN分类算法的性能。Traditional classification methods for hyperspectral remotely sensed images are mostly based on the charac- ter of spectral feature. The spatial features of hyperspectral images are somehow neglected. To make a better use of the spatial information of hyperspectral images, a spatial feature extraction method is proposed based on weighted multi- structure elements mathematical morphological processing without bias. Another spatial feature extraction method based on neighborhood based on the multiscale character of morphological structure is proposed. Then the spatial feature of hy- perspectral is formed. An improved k-NN classification method is proposed based on variable precision rough sets and re- construction error and the method is used to classify hyperspectral image. Experiment results show that the proposed method can classify hyperspectral images successfully.

关 键 词：高光谱遥感图像 空间特征 形态学 k-NN分类算法 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]