加权空-谱与最近邻分类器相结合的高光谱图像分类

黄鸿; 郑新磊

doi:10.3788/OPE.20162404.0873

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加权空-谱与最近邻分类器相结合的高光谱图像分类

信息科学 | 更新时间：2020-08-13

- 加权空-谱与最近邻分类器相结合的高光谱图像分类
- Hyperspectral image classification with combination of weighted spatial-spectral and KNN
- 光学精密工程 2016年24卷第4期页码：873-881
- 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,博士,副教授,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" ]
  [ "郑新磊(1992-),男,山东滨州人,硕士研究生,2014年于重庆大学获得学士学位,主要从事图像处理、遥感影像分类、嵌入式系统等方面的研究。E-mail:zhengxl@cqu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金资助项目(No.41371338,No.61101168)();重庆市基础与前沿研究计划资助项目(No.cstc2013jcyjA4005)();中央高
- DOI：10.3788/OPE.20162404.0873
  中图分类号： TP751.1
- 收稿日期：2015-12-15，
  
  修回日期：2016-01-14，
  
  纸质出版日期：2016-04-25
- 稿件说明：
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黄鸿, 郑新磊,. 加权空-谱与最近邻分类器相结合的高光谱图像分类[J]. 光学精密工程, 2016,24(4): 873-881

HUANG Hong, ZHENG Xin-lei,. Hyperspectral image classification with combination of weighted spatial-spectral and KNN[J]. Editorial Office of Optics and Precision Engineering, 2016,24(4): 873-881
黄鸿, 郑新磊,. 加权空-谱与最近邻分类器相结合的高光谱图像分类[J]. 光学精密工程, 2016,24(4): 873-881 DOI： 10.3788/OPE.20162404.0873.

HUANG Hong, ZHENG Xin-lei,. Hyperspectral image classification with combination of weighted spatial-spectral and KNN[J]. Editorial Office of Optics and Precision Engineering, 2016,24(4): 873-881 DOI： 10.3788/OPE.20162404.0873.

摘要

提出了一种基于加权空-谱距离(WSSD)的相似性度量方法

并将其应用到最近邻分类器(KNN)中

导出了一种新的高光谱图像分类算法。该算法利用高光谱图像的物理特性

通过引入空间窗口和光谱因子这两个参数来挖掘出图像中的空间信息与光谱信息

利用空间近邻点对中心像元进行重构。在最大限度减少图像冗余信息的基础上

增大了同类像元间的相似性以及异类像元间的差异性

获得了更为有效的鉴别特征

从而更好地实现了数据间的相似性度量。基于Indian Pines和PaviaU高光谱数据集进行了实验

结果表明:将提出的WSSD-KNN算法应用于高光谱图像分类时

其分类精度高于其他算法

总体分类精度分别达到了91.72%和96.56%。由于算法较好地融合了图像中的空间-光谱信息

提取出了更为有效的鉴别特征

故不仅有效地改善了高光谱数据的地物分类精度

而且可在训练样本较少时

保持较高的识别率。

Abstract

A spatial consistency measurement method based on the Weighted Spatial-Spectral Distance(WSSD) is proposed and applied to the K Nearest Neighbor(KNN) classifier

and a new hyperspectral image classification algorithm is obtained. On the basis of the physical characters of hyperspectral images

the proposed algorithm combines both spatial window and spectral factor to obtain the spatial information and spectral information

and uses the spatial nearest points to reconstruct the center point and to reveal the local spatial structure. With effectively reducing the redundant information in the image

this algorithm increases the consistency of the same kinds pixels and the difference of the different kinds pixels and obtains extract discriminating features

so it implements the consistency measurement between the data points. The experiments were performed on the Indian Pines and PaviaU hyperspectral data sets. Experiment results show that the WSSD-KNN algorithm has better classification accuracy than other algorithms when it is applied to the classification of hyperspectral image

and the overall classification accuracies reach 91.72% and 96.56%

respectively. With the spectral information

spatial information and extract discriminating features

the proposed algorithm effectively improves ground object classification accuracy of hyperspectral data and has better recognition ability in less train samples.

关键词

Keywords

references

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