IPSJ Transactions on Computer Vision and Applications
Online ISSN : 1882-6695
ISSN-L : 1882-6695
Automatic Martian Dust Storm Detection from Multiple Wavelength Data Based on Decision Level Fusion
Keisuke MaedaTakahiro OgawaMiki Haseyama
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2015 Volume 7 Pages 79-83


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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© 2015 by the Information Processing Society of Japan
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