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小郷原 一智 所属 京都産業大学 理学部 宇宙物理・気象学科 職種 准教授 |
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| 言語種別 | 英語 |
| 発行・発表の年月 | 2016/12 |
| 形態種別 | 研究論文 |
| 査読 | 査読あり |
| 標題 | Automated detection of Martian water ice clouds using Support Vector Machine and simple feature vectors |
| 執筆形態 | その他 |
| 掲載誌名 | PLANETARY AND SPACE SCIENCE |
| 出版社・発行元 | PERGAMON-ELSEVIER SCIENCE LTD |
| 巻・号・頁 | 134,pp.64-70 |
| 著者・共著者 | Kazunori Ogohara,Takafumi Munetomo,Yuji Hatanaka,Susumu Okumura |
| 概要 | We present a method for evaluating the presence of Martian water ice clouds using difference images and cross correlation distributions calculated from blue band images of the Valles Marineris obtained by the Mars Orbiter Camera onboard the Mars Global Surveyor (MGS/MOC). We derived one subtracted image and one cross correlation distribution from two reflectance images. The difference between the maximum and the average, variance, kurtosis, and skewness of the subtracted image were calculated. Those of the cross-correlation distribution were also calculated. These eight statistics were used as feature vectors for training Support Vector Machine because they were the simplest of features that was expected to be closely associated with the physical properties of water ice clouds. The generalization ability was tested using 10-fold cross-validation. F-measure and accuracy tended to be approximately 0.8 if the maximum in the normalized reflectance and the difference of the maximum and the average in the cross-correlation were selected as features. This result can be physically explained because the blue band as well as the red band is sensitive to water ice clouds. A simple and low dimensional feature vector enables us to understand the detected water ice clouds physically and presents the lower bound of the score that classifiers trained using more sophisticated feature vectors have to achieve. |
| DOI | 10.1016/j.pss.2016.10.009 |
| ISSN | 0032-0633 |
| Put Code(ORCID) | 52343296 |
| PermalinkURL | http://www.scopus.com/inward/record.url?eid=2-s2.0-85002637147&partnerID=MN8TOARS |
| researchmap用URL | http://orcid.org/0000-0001-7666-4442 |