面向高光谱图像分类的空谱判别分析

侯榜焕; 姚敏立; 贾维敏; 张峰干; 王道平

doi:10.3788/OPE.20182602.0450

您当前的位置：

首页 >

文章列表页 >

面向高光谱图像分类的空谱判别分析

信息科学 | 更新时间：2020-07-05

- 面向高光谱图像分类的空谱判别分析
- Spatial-spectral discriminant analysis for hyperspectral image classification
- 光学精密工程 2018年26卷第2期页码：450-460
- 作者机构：
  
  火箭军工程大学, 陕西西安 710025
- 作者简介：
  
  [ "侯榜焕(1985-), 男, 陕西咸阳人, 博士研究生, 2006年、2009年于西北工业大学分别获得学士、硕士学位, 主要从事通信与信号处理、高光谱图像处理、机器学习等方面的研究。E-mail:chinayouth001@aliyun.com" ]
  [ "姚敏立(1966-), 男, 山西运城人, 教授, 博士生导师, 1989年、1992年于原第二炮兵工程学院分别获得学士、硕士学位, 1999年于西安交通大学获得博士学位, 主要从事卫星通信等方面的研究。E-mail:yaominli@sohu.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61401471);中国博士后基金资助项目(2014M562636)
- DOI：10.3788/OPE.20182602.0450
  中图分类号： TP751.1
- 收稿日期：2017-06-06，
  
  录用日期：2017-8-15，
  
  纸质出版日期：2018-02-25
- 稿件说明：
移动端阅览
侯榜焕, 姚敏立, 贾维敏, 等. 面向高光谱图像分类的空谱判别分析[J]. 光学精密工程, 2018,26(2):450-460.

Bang-huan HOU, Min-li YAO, Wei-min JIA, et al. Spatial-spectral discriminant analysis for hyperspectral image classification[J]. Optics and precision engineering, 2018, 26(2): 450-460.
侯榜焕, 姚敏立, 贾维敏, 等. 面向高光谱图像分类的空谱判别分析[J]. 光学精密工程, 2018,26(2):450-460. DOI： 10.3788/OPE.20182602.0450.

Bang-huan HOU, Min-li YAO, Wei-min JIA, et al. Spatial-spectral discriminant analysis for hyperspectral image classification[J]. Optics and precision engineering, 2018, 26(2): 450-460. DOI： 10.3788/OPE.20182602.0450.

摘要

针对传统的基于特征提取的高光谱图像地物分类算法大多只考虑光谱信息而忽略空间信息的问题，提出了一种面向高光谱分类的半监督空谱全局与局部判别分析（S

GLDA）算法。该算法首先利用少量标记样本保存数据集的线性可分性和全局判别信息，再依靠较多的无标记的空间局部近邻像元来揭示局部判别信息和非线性局部流形，使高光谱遥感图像的光谱域全局判别结构和空间域局部判别结构在低维特征空间同时得以保留，并在输出特征中自动融入了空间信息，构成了半监督的空谱判别分析。在Indian Pines和PaviaU数据集的实验表明，总体分类精度分别达到76.24%和82.96%。与现有几种算法比较，该算法有效提高了输出特征在低维空间的判别能力，更好地揭示了数据集的内在非线性多模本质，有效提升了高光谱图像数据集的地物分类精度。

Abstract

The traditional hyperspectral image classification methods consider only spectral information while spatial information is ignored. To address this problem

a semi-supervised spatial-spectral global and local discriminant analysis (S

GLDA) algorithm for hyperspectral image classification was proposed. The method firstly made use of a few labeled samples to preserve the linear separability and global discriminant information of the data set

then the local discriminant information and nonlinear manifold was uncovered by the unlabeled spatial neighbors. The spectral-domain global discriminant structure and spatial-domain local discriminant structure were exploited simultaneously and the spatial information was incorporated into the output low-dimension features automatically

which constitute the semi-supervised spatial-spectral discriminant analysis. The overall classification accuracies reached 76.24% and 82.96% on the Indian Pines and PaviaU data sets

respectively. Compared with several existing methods

the proposed algorithm can effectively improve the discriminant ability of the output features in the low-dimension subspace

which can uncover the intrinsic nonlinear multi-modal structure of the data set and obtain higher ground objects classification accuracy.

关键词

Keywords

references

FAUVEL M, TARABALKA Y, BENEDIKTSSON A, et al .. Advances in spectral-spatial classification of hyperspectral images[J]. Proceedings of the IEEE, 2013, 101(3):652-675.

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):873-880.

HUANG H, ZHENG X L. Hyperspectral image classification with combination of weighted spatial-spectral and KNN[J]. Opt. Precision Eng., 2016, 24(4):873-880. (in Chinese)

唐中奇, 付光远, 陈进, 等.基于多尺度分割的高光谱图像稀疏表示与分类[J].光学精密工程, 2015, 23(9):2708-2714.

TANG ZH Q, FU G Y, CHEN J, et al .. Multiscale segmentation-based sparse coding for hyperspectral image classification[J]. Opt. Precision Eng., 2015, 23(9):2708-2714. (in Chinese)

何芳, 王榕, 于强, 等.加权空谱局部保持投影的高光谱图像特征提取[J].光学精密工程, 2017, 25(1):263-273.

HE F, WANG R, YU Q, et al .. Feature extraction of hyperspectral images of weighted spatial and spectral locality preserving projection(WSSLPP)[J]. Opt. Precision Eng., 2017, 25(1):263-273. (in Chinese)

方敏, 王君, 王红艳, 等.应用监督近邻重构分析的高光谱遥感数据特征提取[J].红外与激光工程, 2016, 45(10):1028003.

FANG M, WANG J, WANG H Y, et al .. Feature extraction of hyperspectral remote sensing data using supervised neighbor reconstruction analysis[J]. Infrared and Laser Engineering, 2016, 45(10):1028003. (in Chinese)

邓承志, 张绍全, 汪胜前, 等. L1稀疏正则化的高光谱混合像元分解算法比较[J].红外与激光工程, 2015, 44(3):1092-1097.

DENG CH ZH, ZHANG SH Q, WANG SH Q, et al .. Hyperspectral unmixing algorithm based on L1 regularization[J]. Infrared and Laser Engineering, 2015, 44(3):1092-1097. (in Chinese)

EDWARD J J. A Use's Guide to Principal Components [M]. New York:A Wiley-Interscience Publication, 1992.

DUDA R O, HART P E, STOCK D G. Pattern Classification [M]. 2nd ed. New York, NY, USA:Wiley, 2000.

CAI D, HE X F, HAN J. Semi-supervised discriminant analysis[C]. Proceedings of the 11 th IEEE International Conference on Computer Vision , IEEE , 2007: 1-7.

SUGIYAMA M, IDE T, NAKAJIMA S, et al .. Semi-supervised local fisher discriminant analysis for dimensionality reduction[J]. Machine Learning, 2010, 78(1-2):36-61.

LIAO W, PIZURICA A, SCHEUNDER P, et al .. Semisupervised local discriminant analysis for feature extraction in hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1):184-198.

LUO R B, LIAO W Z, HUANG X, et al .. Feature extraction of hyperspectral images with semisupervised graph learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(9):4389-4399.

ROWEIS S, SAUL L. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500):2323-2326.

YANG Y, XU D, NIE F P, et al .. Image clustering using local discriminant models and global integration[J]. IEEE Transactions on Image Processing, 2010, 19(10):2761-2773.

YANG Y, SHEN H T, MA ZH G, et al . . -norm regularized discriminative feature selection for unsupervised learning[C]. Proceedings of the 22 th International Joint Conference on Artificial Intelligence , AAAI Press , 2011: 1589-1594.

DU L, SHEN ZH Y, LI X, et al . . Local and Global Discriminative Learning for unsupervised feature selection[C]. Proceedings of the 13 th IEEE International Conference on Data Mining , IEEE , 2013: 131-140.

DU X ZH, YAN Y, PAN P B, et al .. Multiple graph unsupervised feature selection[J]. Signal Processing, 2016, 120:754-760.

ZHANG L F, ZHANG Q, DU B, et al .. Simultaneous spectral-spatial feature selection and extraction for hyperspectral images[J]. IEEE Transactions on Cybernetics, 2017, 48(1):16-28.

XIA J S, BOMBRUN L, ADALI T, et al .. Spectral-spatial classification of hyperspectral data using ICA and edge preserving filtering via an ensemble strategy[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8):4971-4981.

PU H, CHEN Z, WANG B, et al .. A Novel Spatial-spectral similarity measure for dimensionality reduction and classification of hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11):7008-7022.

DALTON L, SAURABH P, MELBA M C, et al .. Manifold-learning based feature extraction for classification of hyperspectral data:a review of advances in manifold learning[J]. IEEE Signal Processing Magazine, 2014, 31(1):55-66.

浏览量

397

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

半监督多图嵌入的高光谱影像特征提取

半监督式野生动物夜间目标端到端检测

基于分数阶微分的高光谱图像特征提取与分类

面向高光谱影像场景分类的轻量化深度全局-局部知识蒸馏网络

采用SVD协同训练的半监督实例级目标检测