• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of image and data fusion >Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques
【24h】

Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques

机译：使用基于有监督和无监督特征提取技术的扩展属性配置文件对高光谱数据进行分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The classification of remote sensing data based on the exploitation of spatial features extracted with morphological and attribute profiles has been recently gaining importance. With the development of efficient algorithms to construct the profiles for large datasets, such methods are becoming even more relevant. When dealing with hyperspectral imagery, the profiles are traditionally built on the first few principal components computed from the data. However, it needs to be determined if other feature reduction approaches are better suited to create base images for the profiles. In this article, we explore the use of profiles based on features derived from three supervised feature extraction techniques (i.e. Discriminant Analysis Feature Extraction, Decision Boundary Feature Extraction and Non-parametric Weighted Feature Extraction) and two unsupervised feature-extraction techniques (i.e. Principal Component Analysis (PCA) and Kernel PCA) in classification and compare the classification accuracies obtained by using different techniques for two different classification methods. The obtained results indicate significant improvements in the accuracies using the supervised feature extraction methods. However, the choice of the method affects the quality of the results for different datasets depending on the availability of the training samples.
机译：基于利用形态学和属性轮廓提取的空间特征的遥感数据分类近来变得越来越重要。随着高效算法的发展，以构造大型数据集的配置文件，这种方法变得越来越重要。在处理高光谱图像时，配置文件通常建立在根据数据计算的前几个主要成分上。但是，需要确定其他特征缩小方法是否更适合为配置文件创建基础图像。在本文中，我们探索基于基于三种监督特征提取技术（即判别分析特征提取，决策边界特征提取和非参数加权特征提取）和两种无监督特征提取技术（即主成分）的特征的配置文件的使用分析（PCA）和内核PCA），并比较针对两种不同分类方法使用不同技术获得的分类准确性。获得的结果表明使用监督特征提取方法的准确性有了显着提高。但是，方法的选择会影响不同数据集的结果质量，具体取决于训练样本的可用性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号