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A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data
المؤلفون المشاركون
Sharma, Ram C.
Hara, Keitarou
Hirayama, Hidetake
المصدر
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-06-11
دولة النشر
مصر
عدد الصفحات
8
التخصصات الرئيسية
الملخص EN
This paper presents the performance and evaluation of a number of machine learning classifiers for the discrimination between the vegetation physiognomic classes using the satellite based time-series of the surface reflectance data.
Discrimination of six vegetation physiognomic classes, Evergreen Coniferous Forest, Evergreen Broadleaf Forest, Deciduous Coniferous Forest, Deciduous Broadleaf Forest, Shrubs, and Herbs, was dealt with in the research.
Rich-feature data were prepared from time-series of the satellite data for the discrimination and cross-validation of the vegetation physiognomic types using machine learning approach.
A set of machine learning experiments comprised of a number of supervised classifiers with different model parameters was conducted to assess how the discrimination of vegetation physiognomic classes varies with classifiers, input features, and ground truth data size.
The performance of each experiment was evaluated by using the 10-fold cross-validation method.
Experiment using the Random Forests classifier provided highest overall accuracy (0.81) and kappa coefficient (0.78).
However, accuracy metrics did not vary much with experiments.
Accuracy metrics were found to be very sensitive to input features and size of ground truth data.
The results obtained in the research are expected to be useful for improving the vegetation physiognomic mapping in Japan.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sharma, Ram C.& Hara, Keitarou& Hirayama, Hidetake. 2017. A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data. Scientifica،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1202690
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sharma, Ram C.…[et al.]. A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data. Scientifica No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1202690
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sharma, Ram C.& Hara, Keitarou& Hirayama, Hidetake. A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data. Scientifica. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1202690
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1202690
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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