空-谱协同流形重构的高光谱影像分类

黄鸿; 陈美利; 段宇乐; 石光耀

doi:10.3788/OPE.20182607.1827

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空-谱协同流形重构的高光谱影像分类

信息科学 | 更新时间：2020-08-13

- 空-谱协同流形重构的高光谱影像分类
- Hyper-spectral image classification using spatial-spectral manifold reconstruction
- 光学精密工程 2018年26卷第7期页码：1827-1836
- 作者机构：
  
  重庆大学光电技术与系统教育部重点实验室, 重庆 400044
- 作者简介：
  
  [ "黄鸿(1980-), 男, 湖南新宁人, 教授, 博士生导师, 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。E-mail:hhuang@cqu.edu.cn" ]
  [ "陈美利(1992-), 女, 重庆铜梁人, 硕士研究生, 2016年于南京师范大学获得学士学位, 主要从事图像处理、遥感影像分类等方面的研究。E-mail:chenmeili@cqu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金资助项目(41371338)
- DOI：10.3788/OPE.20182607.1827
  中图分类号： TP394.1;TH691.9
- 收稿日期：2017-11-03，
  
  录用日期：2018-1-19，
  
  纸质出版日期：2018-07-25
- 稿件说明：
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黄鸿, 陈美利, 段宇乐, 等. 空-谱协同流形重构的高光谱影像分类[J]. 光学精密工程, 2018,26(7):1827-1836.

Hong HUANG, Mei-li CHEN, Yu-le DUAN, et al. Hyper-spectral image classification using spatial-spectral manifold reconstruction[J]. Optics and precision engineering, 2018, 26(7): 1827-1836.
黄鸿, 陈美利, 段宇乐, 等. 空-谱协同流形重构的高光谱影像分类[J]. 光学精密工程, 2018,26(7):1827-1836. DOI： 10.3788/OPE.20182607.1827.

Hong HUANG, Mei-li CHEN, Yu-le DUAN, et al. Hyper-spectral image classification using spatial-spectral manifold reconstruction[J]. Optics and precision engineering, 2018, 26(7): 1827-1836. DOI： 10.3788/OPE.20182607.1827.

摘要

鉴于传统高光谱影像分类大都采用监督学习方法，且仅利用了光谱信息，未考虑影像空间特征和流形结构。提出一种基于空-谱协同流形重构误差的高光谱影像分类方法，该算法基于高光谱影像中地物分布的空间一致性，利用少量标记的样本和大量的无标记空间近邻样本来进行半监督学习，并利用测试样本在每一子流形上的重构误差来表征相似性，实现鉴别分类。在Indian Pines和University of Pavia数据集上的实验结果表明，本文方法的分类精度在各种条件下要优于其他分类算法，其最高总体精度分别达到了95.67%和91.92%。该算法将高光谱遥感影像中的空间-光谱信息融入不同地物的子流形结构表征，在训练样本数量较少时仍能得到好的分类效果，有效提升了分类性能。

Abstract

In recent years

several supervised learning methods have been introduced in hyperspectral image (HSI) classification. However

these methods use only spectral information without taking into account the spatial features and manifold structures of HSIs. To overcome this problem

a new classification method was proposed for HSI classification

combining spatial-spectral features and manifold reconstruction. Based on the spatial consistency of ground objects distribution in HSIs

the proposed algorithm used a small number of labeled samples and large number of unlabeled spatial neighbor samples to perform semisupervised learning

and utilized the reconstruction error of test samples in each submanifold to represent the similarities for discriminant classification. Experimental results obtained from the Indian Pines and University of Pavia data set reveal that the proposed method exhibits a higher classification accuracy compared to other classification algorithms under various training conditions

the highest overall accuracy achieved in the two cases being 95.67% and 91.92%

respectively. The proposed method integrates spatial-spectral information to represent the submanifold structure of different land objects

exhibits superior discrimination performance

especially for a small number of training samples

and effectively improves the performance of HSI classification.

关键词

Keywords

references

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