• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on New Trends in Information Science, Service Science and Data Mining >Research on multisource remote sensing image classification algorithms based on image fusion and the EM-HMRF
【24h】

Research on multisource remote sensing image classification algorithms based on image fusion and the EM-HMRF

机译：基于图像融合和EM-HMRF的多源遥感图像分类算法研究

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

摘要

Aiming at classifying multisource remote sensing images, we first introduce a Markov Random Field (MRF) to build prior probability models for multiple object classes. The Expectation Maximization-Hierarchical Markov Random Field (EM-HMRF) algorithm is then introduced to take advantage of the equivalence relation between the EM-HMRF and the fuzzy classification method. Second, this paper focused on exploiting self-adaptivity for selecting the prior distribution model parameter β automatically, and then two fusion schemes (centralized-based and distributed-based fusion) are introduced to achieve better classification results. A new algorithm is derived for supporting multisource remote sensing image classification by using image fusion and the EM-HMRF. The experimental results on synthetic images and real remote sensing images indicate that our proposed algorithm with two fusion schemes can not only greatly improve the accuracy of image classification but also strengthen the anti-interference of noise, thereby providing good evidence to support the effectiveness and superiority of our proposed algorithm in solving multisource remote sensing image classification problems. Our proposed algorithm for image classification with a fusion scheme should have great potential value for multisource remote sensing image classification strategies.
机译：旨在分类MultiSource遥感图像，我们首先介绍Markov随机字段（MRF），为多个对象类构建现有概率模型。然后引入期望最大化 - 分层马尔科夫随机字段（EM-HMRF）算法以利用EM-HMRF与模糊分类方法之间的等效关系。其次，本文集中于自适应利用自适应，自动选择先前分配模型参数β，然后引入两个融合方案（基于集中式和分布式的融合）以实现更好的分类结果。导出了一种新的算法，用于通过使用图像融合和EM-HMRF来支持多源遥感图像分类。合成图像和真实遥感图像的实验结果表明，我们的建议算法具有两个融合方案，不仅可以大大提高图像分类的准确性，而且还强化噪声的抗干扰，从而提供了支持效果和优越性的良好证据关于求解多源遥感图像分类问题的建议算法。我们具有融合方案的图像分类算法应具有很大的潜在价值，用于多源遥感图像分类策略。

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