| 1. | This method is a combination of the polar decomposition and the
该方法是ica和基于模糊集理论的非监督分类方法的结合。 |
| 2. | Besides , we study an unsupervised classification method based on polar decomposition
我们接着研究了基于极化分解的非监督分类方法。 |
| 3. | In this dissertation , we study an unsupervised classification method based on fuzzy set theory
我们首先研究了基于模糊集理论的非监督分类方法。 |
| 4. | This method is a combination of the usage of polarimetric information of polsar data and the unsupervised classification method based on fuzzy set theory
该方法是原始sar数据极化信息的利用和基于模糊集理论的非监督分类方法的结合。 |
| 5. | The image of nanchuan in 2000 was interpreted using a multi - extracting method combining supervised classification with non - supervised classification
利用监督分类和非监督分类相结合的分层提取分类方法对南川市2000年遥感影像进行解译。 |
| 6. | The image of nanchuan in 2000 was interpreted using a multi - extracting method combining supervised classification with non - supervised classification
摘要利用监督分类和非监督分类相结合的分层提取分类方法对南川市2000年遥感影像进行解译。 |
| 7. | In this dissertation , we study some unsupervised classification methods through three aspects , such as preprocessing , the selection of classifier and feature extracting
为了得到更快速、更准确的分类结果,本文围绕预处理、分类器的选择和特征提取等问题研究了一些非监督分类方法。 |
| 8. | The experimental results show that these two classification methods of multi - sources information fusion can result in better accuracy than that of conventional unsupervised classification method
实验结果表明基于bdset和fdset融合的分类方法比传统的非监督分类方法具有更好的分类效果，有效地提高了分类的精度。 |
| 9. | In the end , multispectral image fusion algorithms based on principal component analysis and clustering algorithms are introduced . multispectral image fusion based on unsupervised classification is realized
最后，还介绍了基于主成分分析( pca )的多光谱图象融合方法以及非监督分类的方法，并实现了基于非监督分类的多光谱图象融合算法。 |
| 10. | And then the paper describes em - mrf iterative algorithm and its realization for the parameter estimation in unsupervised image classiifcation process . the em - mrf - based image classification strategy is introduced into multisensor feature - level image fusion , distributed and centric based fusion methods are proposed . finally , simulated results through sythetic and real remotely sensed image illustrate the effectiveness and advantage of the proposed methods
针对遥感图像非监督分类中的参数估计问题，重点讨论了em - mrf迭代算法的原理和实现，并将em - mrf迭代算法引入到多源遥感图像融合的过程中，提出了两种分别基于集中式融合模型和分布式融合模型的图像融合方法。 |
