摘要
随着高光谱图像技术的发展,高光谱图像在众多领域得到了广泛应用.高光谱图像分类是其应用领域的一个重要分支,其中高精度的分类算法则是实现准确分类的前提.高光谱图像分辨率高、波段数多、数据量大等特点给传统分类技术带来了巨大挑战.该文综述了基于高光谱图像的支持向量机分类法、人工神经元网络分类法、决策树分类法、最大似然分类法等监督分类方法以及K-均值聚类法和迭代自组织方法等非监督分类方法,并结合实际高光谱图像数据给出应用实例.基于不同应用需求,以上两类分类方法均能最大程度地挖掘高光谱图像的图谱信息,从而实现更加准确和精细的模式识别.
With the development of hyperspectral image technology,hyperspectral image analysis has been widely applied in many fields.The classification of hyperspectral images is an important branch in the whole process.The higher precision is the aim we are pursuing after while the classification algorithm is the vital premise.For hyperspectral image,versus the traditional classification techniques,the characteristics such as high resolution in spectrum and cube data structure have brought great challenges.Based on extracted feature mentioned in former paper,the support vector machine,artificial neural network,decision tree,maximum likelihood method are discussed,which belong to supervised state.Meanwhile,the K-mean clustering method and iterative self-organizing method which belong to unsupervised state are also involved.Finally,the application examples are also listed to illuminate the theory.Based on different application requirements,the above of two category classification methods(supervised and unsupervised)can dig the information to the largest extent and attain more accurate result.
出处
《广西师范学院学报（自然科学版）》
2015年第3期38-44,共7页
Journal of Guangxi Teachers Education University(Natural Science Edition)
基金
广西教育厅项目(201203YB103)
关键词
高光谱图像
支持向量机
人工神经元网络
决策树分类
最大似然分类法
K-均值聚类法
迭代自组织方法
hyperspectral image
Support Vector Machine
Artificial Neural Network
Decision Tree
Maximum Likelihood Classification
K-mean cluster
iterative self-organizing method
