一种基于空间信息和遗传算法的半监督高光谱图像分类方法

doi:10.13474/j.cnki.11-2246.2018.0308

测绘通报 ›› 2018, Vol. 0 ›› Issue (10): 22-26.doi: 10.13474/j.cnki.11-2246.2018.0308

一种基于空间信息和遗传算法的半监督高光谱图像分类方法

胡冬翠¹, 谢福鼎¹, 杨俊¹, 张永²

1. 辽宁师范大学城市与环境学院, 辽宁大连 116029;
2. 辽宁师范大学计算机与信息技术学院, 辽宁大连 116029

收稿日期:2017-12-09 修回日期:2018-06-12 出版日期:2018-10-25 发布日期:2018-10-31
作者简介:胡冬翠(1992-),女,硕士生,主要从事空间数据挖掘方面的研究。E-mail:576449958@qq.com
基金资助:
国家自然科学基金（41771178；61772252）

A Method of Semi-supervised Classification for Hyperspectral Images Based on Spatial Information and Genetic Optimization

HU Dongcui¹, XIE Fuding¹, YANG Jun¹, ZHANG Yong²

1. College of Urban and Environment, Liaoning Normal University, Dalian 116029, China;
2. College of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China

Received:2017-12-09 Revised:2018-06-12 Online:2018-10-25 Published:2018-10-31

摘要/Abstract

摘要： 在诸多的降维方法中，由于谱方法实现简单，近年来得到了广泛的应用。在谱方法中，图的构造及相似度函数的选择是影响降维效果的关键因素。本文基于像素点的空间近邻信息、遗传算法、谱方法和少量标签样本点，提出了一种半监督的高光谱图像分类方法。首先通过考虑样本点的空间信息和少量有标签样本点的类信息，构造了新的相似度函数；然后用K近邻方法和遗传算法得到优化图，基于优化的图，用谱方法进行数据降维；最后通过局部平均伪近邻方法进行聚类分析，并在两个经典的高光谱图像SalinasA数据集和Botswana数据集上进行测试，结果表明本文提出的方法能达到较高的分类精度。

关键词: 谱聚类, 遗传算法, 空间信息, 高光谱图像, 半监督分类

Abstract: Among many dimensionality reduction methods,because of the simplicity of spectral methods,it has been widely applied in recent years.In the spectral method,the construction of graph and the selection of similarity function are the key factors affecting the dimensionality reduction effect.A semi-supervised hyperspectral image classification method is proposed based on spatial nearest neighbor information,genetic algorithm,spectral method and a small number of label sample points.The algorithm firstly constructs a new similarity function by considering the spatial information of the sample points and the class information of a small number of labeled sample points.Then K-nearest neighbor method and genetic algorithm are used to obtain the optimal graph.Based on the optimized graph,the spectral method is used to reduce the dimension of the data.Final adoption the local average pseudo-nearest neighbor method is applied to cluster analysis.In this paper,two classical hyperspectral images SalinasA data sets and Botswana data sets are tested.The results show that the proposed method can achieve high classification accuracy.

Key words: spectral clustering, genetic algorithm, spatial information, hyperspectral image, semi-supervised classification

中图分类号:

P237

胡冬翠, 谢福鼎, 杨俊, 张永. 一种基于空间信息和遗传算法的半监督高光谱图像分类方法[J]. 测绘通报, 2018, 0(10): 22-26.

HU Dongcui, XIE Fuding, YANG Jun, ZHANG Yong. A Method of Semi-supervised Classification for Hyperspectral Images Based on Spatial Information and Genetic Optimization[J]. 测绘通报, 2018, 0(10): 22-26.

参考文献

[1] 田彦平,陶超,邹峥嵘,等.主动学习与图的半监督相结合的高光谱影像分类[J].测绘学报,2015,44(8):919-926.
[2] 丁胜,袁修孝,陈黎.粒子群优化算法用于高光谱遥感影像分类的自动波段选择[J].测绘学报,2010,39(3):257-263.
[3] SCHAEFFER S E.Survey:Graph Clustering[J].Computer Science Review,2007,1(1):27-64.
[4] CHRYSOULI C,TEFAS A.Spectral Clustering and Semi-supervised Learning Using Evolving Similarity Graphs[J].Applied Soft Computing,2015,34(C):625-637.
[5] 杨钊霞,邹峥嵘,陶超,等.空-谱信息与稀疏表示相结合的高光谱遥感影像分类[J].测绘学报,2015,44(7):775-781.
[6] LUXBURG U.A Tutorial on Spectral Clustering[J].Statistics and Computing,2007,17(4):395-416.
[7] GOU J,DU L,ZHANG Y,et al.A New Distance-weighted K-nearest Neighbor Classifier[J].Journal of Information and Computational Science,2012,9(6):1429-1436.
[8] GOU J,ZHAN Y,RAO Y,et al.Improved Pseudo Nearest Neighbor Classification[J].Knowledge-based Systems,2014,70(C):361-375.
[9] SABA F,VALADANZOU M J,MOKHTARZADE M.The Optimazation of Multi Resolution Segmentation of Remotely Sensed Data Using Genetic Alghorithm[J].ISPRS-international Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2013,40(1):345-349.
[10] 何芳,贾维敏,王标标,等.基于加权空-谱联合的遥感图像聚类[C]//国家安全地球物理丛书(十二)——地球物理与信息感知.鞍山:[s.n.],2016:5.
[11] BELKIN M,NIYOGI P,SINDHWANI V.Manifold Regularization:A Geometric Framework for Learning from Labeled and Unlabeled Examples[J].Journal of Machine Learning Research,2006,7(1):2399-2434.
[12] CAMPS-VALLS G,MARSHEVA T V B,Zhou D.Semi-supervised Graph-based Hyperspectral Image Classification[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(10):3044-3054.
[13] 邬文慧.空谱联合高光谱遥感图像半监督分类[D].西安:西安电子科技大学,2014.
[14] XIE J,GUO W,XIE W,et al.K-means Clustering Algorithm Based on Optimal Initial Centers Related to Pattern Distribution of Samples in Space[J].Applications Research of Computers,2012,29(3):888-892.
[15] KANNAN S R,RAMATHILAGAM S,CHUNG P C.Effective Fuzzy C-means Clustering Algorithms for Data Clustering Problems[J].Expert Systems with Applications,2012,39(7):6292-6300.
[16] 李志敏,郝盼超,黄鸿,等.半监督复合核图聚类在高光谱图像中的应用[J].光电工程,2016,44(4):33-39.

一种基于空间信息和遗传算法的半监督高光谱图像分类方法

A Method of Semi-supervised Classification for Hyperspectral Images Based on Spatial Information and Genetic Optimization

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