Comparison of SVM and RF Algorithms for Crop Mapping Using Bi-Temporal Optical and Radar Data with Limited Training Samples

Publish Year: 1396
نوع سند: مقاله کنفرانسی
زبان: English
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COMCONF05_720

تاریخ نمایه سازی: 21 اردیبهشت 1397

Abstract:

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.

Authors

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