| 多层级二值模式的高光谱影像空-谱分类 |
| |
| 引用本文： | 职露,余旭初,邹滨,刘冰.多层级二值模式的高光谱影像空-谱分类[J].武汉大学学报(信息科学版),2019,44(11):1659-1666. |
| |
| 作者姓名： | 职露 余旭初 邹滨 刘冰 |
| |
| 作者单位： | 1.华北水利水电大学测绘与地理信息学院, 河南 郑州, 450001 |
| |
| 基金项目： | 国家重点研发计划2016YFC0206205 |
| |
| 摘 要： | 利用高光谱遥感影像的空间纹理特征，可以提高高光谱遥感影像的分类精度。提出了一种多层级二值模式的高光谱影像空-谱联合分类方法。该方法将高光谱影像转化为局部二值模式特征图像获取像元微观特征，基于特征图像生成多层级特征向量获取像元宏观特征。为验证该方法的有效性，选取PaviaU、Salinas和Chikusei高光谱影像数据，利用核极限学习机分类器，分别针对光谱、局部二值模式、多层级二值模式等特征开展实验。结果表明，多层级二值模式空-谱分类总体精度分别达到97.31%、98.96%和97.85%，明显优于传统光谱、3Gabor空-谱等分类方法。该方法可为高光谱影像分类提供更加有效的类别判定特征，有助于提高影像分类精度并获取更加平滑的分类结果图。
|
| 关 键 词： | 高光谱遥感影像 空-谱分类 多层级二值模式 核极限学习机 |
| 收稿时间： | 2018-06-04 |
A Multi-Layer Binary Pattern Based Method for Hyperspectral Imagery Classification Using Combined Spatial-Spectral Characteristics |
| |
| Affiliation: | 1.College of Surveying and Geo-informatics, North China University of Water Resources and Electric Power, Zhengzhou 450001, China2.Information Engineering University, Zhengzhou 450001, China3.Department of Geo-informatics, Central South University, Changsha 410083, China |
| |
| Abstract: | It is essential to make full use of the spatial features for hyperspectral imagery classification. A multi-layer binary pattern based method for hyperspectral remote sensing imagery classification using combined spatial-spectral characteristics is proposed. Firstly, the hyperspectral remote sensing imagery was transformed to local binary pattern image reflecting the micro-structures of the imagery, and multi-layer feature vectors were acquired from the local binary pattern image reflecting a wider range of the imagery structure. Here, in order to verify the proposed method, the PaviaU, the Salinas and the Chikusei imageries were used and nine kinds of features such as spectral features, local binary pattern spatial features, the multi-resolution local binary pattern features, etc., were employed in the experiment for hyperspectral remote sensing imagery spatial-spectral classification.The classifier of this algorithm used the kernel extreme learning machine classifier. The overall classification accuracies separately reached 97.31%, 98.96% and 97.85% based on the multi-layer binary patterns spatial-spectral features which were superior to the traditional methods such as spectral classification and spatial-spectral classification using 3Gabor features. The results indicate that the proposed approach can provide the effectively distinguished features showing the higher classification accuracy and the smoother classification map. |
| |
| Keywords: | |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《武汉大学学报(信息科学版)》浏览原始摘要信息 |
|
点击此处可从《武汉大学学报(信息科学版)》下载全文 |
|
