چکیده مقاله
This paper aims to compare two state of the art classification algorithms, namely Support Vector Machine SVM and Random Forest RF algorithms, in terms of accuracy and running time, in order to crop mapping from multi temporal optical and radar images with limited training samples The optical data are RapidEye images and the radar data are UAVSAR images The case study is an agricultural area near Winnipeg, Manitoba, Canada From each RapidEye image, 38 optical features, and from each UAVSAR image, 49 radar features were extracted The results indicated RF was more efficient in the classification of radar features, while SVM was more efficient in the classification of optical and stacked features Furthermore, regarding running time, RF was much faster than SVM in all scenarios
کلیدواژهها
نویسندگان
Iman Khosravi
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, I R Iran
Saeid Niazmardi
Faculty of Civil & Surveying Engineering, Graduate University of Advanced Technology, Kerman, I R Iran
Abdolreza Safari
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, I R Iran
Saeid Homayouni
Department of Geography, Environment, and Geomatics, University of Ottawa, Ottawa, Canada
شیوه ارجاع
Khosravi, Iman and Niazmardi, Saeid and Safari, Abdolreza and Homayouni, Saeid,1396,Comparison of SVM and RF Algorithms for Crop Mapping Using Bi-Temporal Optical and Radar Data with Limited Training Samples,Fifth International Conference on Electrical and Computer Engineering with Emphasis on Indigenous Knowledge,Tehran
ارائهشده در
مجموعه مقالات پنجمین کنفرانس بین المللی مهندسی برق و کامپیوتر با تاکید بر دانش بومی19 بهمن 1396 · تهران