Automatic Martian Dust Storm Detection from Multiple Wavelength Data Based on Decision Level Fusion

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書誌事項

公開日
2015
資源種別
journal article
DOI
  • 10.2197/ipsjtcva.7.79
公開者
一般社団法人 情報処理学会

説明

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.

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