首页    期刊浏览 2025年06月11日 星期三
登录注册

文章基本信息

  • 标题:Automatic Martian Dust Storm Detection from Multiple Wavelength Data Based on Decision Level Fusion
  • 本地全文:下载
  • 作者:Keisuke Maeda ; Takahiro Ogawa ; Miki Haseyama
  • 期刊名称:Information and Media Technologies
  • 电子版ISSN:1881-0896
  • 出版年度:2015
  • 卷号:10
  • 期号:3
  • 页码:473-477
  • DOI:10.11185/imt.10.473
  • 出版社:Information and Media Technologies Editorial Board
  • 摘要:This paper presents automatic Martian dust storm detection from multiple wavelength data based on decision level fusion. In our proposed method, visual features are first extracted from multiple wavelength data, and optimal features are selected for Martian dust storm detection based on the minimal-Redundancy-Maximal-Relevance algorithm. Second, the selected visual features are used to train the Support Vector Machine classifiers that are constructed on each data. Furthermore, as a main contribution of this paper, the proposed method integrates the multiple detection results obtained from heterogeneous data based on decision level fusion, while considering each classifier's detection performance to obtain accurate final detection results. Consequently, the proposed method realizes successful Martian dust storm detection.
  • 关键词:Mars;detection;dust storm;decision level fusion
国家哲学社会科学文献中心版权所有