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面向高光?#23376;?#20687;分类的多特征流形鉴别嵌入

Multi-features manifold discriminant embedding for hyperspectral image classification

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摘要

鉴于传统维数?#25216;?#26041;法对高光谱遥感影像进行降维时, 往往只利用了单一的光谱特征, 限制了分类性能的提升。提出一?#21482;?#20110;多特征流形鉴别嵌入的维数?#25216;?#26041;法, 该方法首先提取高光?#36164;?#25454;的LBP(Local Binary Patterns)纹理特征, 然后利用样本点的光谱-LBP特征联合距离及类别信息构建类内图和类间?#23478;?#21457;现高光?#23376;?#20687;中的鉴别流形结构, 在低维嵌入空间中不仅保持来自同一像素的光谱和纹理特征的相似性, 而且使同类点尽可能紧致、不同类点远离, 实现空-谱联?#31995;?#32500;鉴别特征提取, ?#26434;行?#25552;高地物分类性能。在Indian Pines和黑河高光谱遥感数据集上的实验表明, 本文算法的分类精度在不同实验条件下均优于传统的维数?#25216;?#26041;法, 其分类精度可达95.05%和96.20%, 在较少训?#36153;?#26412;条件下优?#32856;?#20026;明显, 有利于实际应用。

Abstract

The traditional Dimensionality Reduction (DR) methods consider the spectral features but ignores useful spatial information in HSI. To overcome this problem, this paper proposed a new dimensionality reduction method called Multi-Feature Manifold Discriminant Embedding (MFDE). First, the MFDE method extracted the features of the local binary pattern from HSI data. Next, the with-class and between-class graphs were constructed using sample labels to exploit the local manifold structure. Then, an optimal object function was designed to learn the combined spatial-spectral features by compacting the intra-class samples and simultaneously separating the inter-class samples. Thus, the discriminative ability of embedding features was improved. Experimental results in the Indian Pines and Heihe hyperspectral data sets show that the proposed MFDE method performs better than some state-of-the-art DR methods in most cases and achieves an overall classification accuracy of 95.05% and 96.20%, respectively. Its advantage is more significant for less training samples, making it more conducive to practical applications.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP394.1;TH691.9

DOI:10.3788/ope.20192703.0726

所属栏目:信息科学

基金项目:国家自然科学基金资助项目(No.41371338); 重庆市基础研究与前沿探索项目资助(No.cstc2018jcyjAX0093); 重庆市研究生科研创新项目资助(No.CYS18035)

收稿日期:2018-09-14

修改稿日期:2018-11-16

网络出版日期:--

作者单位    点击查看

黄 鸿:重庆大学 光电技术与系统教育部重点实验室, 重庆 400044
李政英:重庆大学 光电技术与系统教育部重点实验室, 重庆 400044
石光耀:重庆大学 光电技术与系统教育部重点实验室, 重庆 400044
潘银松:重庆大学 光电技术与系统教育部重点实验室, 重庆 400044

联系人作者:黄鸿([email protected])

备注:黄 鸿(1980-), 男, 湖南新宁人, 教授, 博?#21487;?#23548;师, 2003, 2005, 2008年于重庆大学分别获得学士、硕士和博士学位, 主要从事流形学习、模式识别、遥感影像智能化处理等方面的研究。

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引用该论文

HUANG Hong,LI Zheng-ying,SHI Guang-yao,PAN Yin-song. Multi-features manifold discriminant embedding for hyperspectral image classification[J]. Optics and Precision Engineering, 2019, 27(3): 726-738

黄 鸿,李政英,石光耀,潘银松. 面向高光?#23376;?#20687;分类的多特征流形鉴别嵌入[J]. 光学 精密工程, 2019, 27(3): 726-738

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