数字农科院2.0

Kharif Dryland Crop Identification Based on Synthetic Aperture Radar in the North China Plain

文献类型： 外文期刊

作者： Dong Zhaoxia;Wang Di;Zhou Qingbo;Chen Zhongxin

作者机构：

关键词： Synthetic Aperture Radar (SAR);dry-land crop identification;decision tree classifier;support vector machine

期刊名称： 2015 FOURTH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS

ISSN： 2334-3168

年卷期： 2015 年

页码：

收录情况： SCI

摘要： During the key growth period of kharif dry-land crops in the north of China, due to the big impact of cloudy or rainy weather, it's impossible to acquire optical remote sensing data in a timely and efficient manner. Therefore, it's very necessary to use radar remote sensing to identify kharif dry-land crops. With Shenzhou City of Hubei Province as the study area, this paper has selected 6 sessions of Radarsat-2 fully polarimetric images which cover the area from June 3rd to Oct 1st in 2014 as the data source. Through the analysis of the backward-scattering characteristic of various ground objects, we found that cross-polarization channels had a better performance than like-polarization in identifying dry-land crops. Also, we put forward the optimum polarization and phase for identifying dry-land crops with the support vector machine (SVM) classification accuracy and Jeffries-Matusita (J-M) distance as the standard. We conducted identification of 5 major ground objects in the study area with the decision tree classifier (DTC) and the SVM method. The result suggests that radar data can be effectively applied in identifying dry-land crops, SVM is superior to DTC in identifying dry-land crops and SVM has a distinct advantage in identifying small areas and controlling speckle noise.

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