کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5488076 | 1523906 | 2016 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Automated detection of Martian water ice clouds using Support Vector Machine and simple feature vectors
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
فیزیک زمین (ژئو فیزیک)
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Planetary and Space Science - Volume 134, 1 December 2016, Pages 64-70
Journal: Planetary and Space Science - Volume 134, 1 December 2016, Pages 64-70
نویسندگان
Kazunori Ogohara, Takafumi Munetomo, Yuji Hatanaka, Susumu Okumura,