• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>European Journal of Remote Sensing >Multiple Classifier Ensembles with Band Clustering for Hyperspectral Image Classification
【24h】

Multiple Classifier Ensembles with Band Clustering for Hyperspectral Image Classification

机译：带波段聚类的多个分类器集合，用于高光谱图像分类

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Due to the high dimensionality of a hyperspectral image, classification accuracy of a single classifier may be limited when the size of the training set is small. A divide-and-conquer approach has been proposed, where a classifier is applied to each group of bands and the final output will be the fused result of multiple classifiers. Since the dimensionality in each band group is much lower, classification accuracy of the overall system can be improved even when training samples are limited. In this paper, we proposed a new multiple classifier ensembles which using SKMd-based band clustering features as input. We also investigate the impact of band partition for this approach. We find out that band partition based on spectral clustering (resulting in band groups composed of non-consecutive bands) can outperform the partition based on spectral correlation coefficient (resulting in band groups composed of consecutive bands only), in particular when the number of training samples is small.
机译：由于高光谱图像的高维度，当训练集的大小小时，单个分类器的分类精度可能受到限制。已经提出了分而治之的方法，其中将分类器应用于每组频带，并且最终输出将是多个分类器的融合结果。由于每个频带组中的维数要低得多，因此即使训练样本有限，也可以提高整个系统的分类精度。在本文中，我们提出了一种新的多重分类器集成，它使用基于SKMd的频带聚类特征作为输入。我们还研究了这种方法的频带划分的影响。我们发现基于频谱聚类的频带划分（导致由非连续频带组成的频带组）可以胜过基于频谱相关系数的划分（仅导致由连续频带组成的频带组），特别是在训练次数样品很小。

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