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Capability of Decomposition Methods for Identification of Crops and Other Land-cover Targets using Hybrid Polarimetric SAR Data

机译:使用混合极化SAR数据的分解方法用于识别农作物和其他土地被覆目标的能力

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The main objective of current study is to investigate the potential of decomposition methods for monitoring crops and discrimination of other land cover targets using c-band hybrid polarimetric Risat-1 SAR images. There are two study areas namely Burdwan and Bharatpur in India chosen for analysing various existing decomposition methods in this paper. The Risat-1 hybrid polarimetric SAR Single look complex (SLC) data by ascending observation mode were utilized in our experiment and acquired in the month of December 22nd, 2014 and August 3rd, 2016 from parts of Burdwan and Bharatpur area respectively. The Stokes classical parameters G_0, G_1, G_2 and G_3 are derived from hybrid SAR images for further analysis. From these Stokes parameters, the relative phase, degree of polarization, orientation, ellipticity and polarization angle are calculated. Furthermore, four decomposition techniques namely m-δ, m-α, m-x and modified m-x are performed and expressed in the form of odd bounce, even bounce and volume components for monitoring crops and surrounding land cover targets in our study areas. The preliminary results have been observed from supervised classification on the basis of decomposition methods for identification of various crops, barren land, urban areas and waterbodies in the study sites. It has been shown that volume component among all decompositions is over estimated in comparison to odd and even bounce components. Risat-1 hybrid SAR data is found to be more suitable, convenient and cost-effective for discrimination of various land cover targets whereas cloud free optical data is a prime hindrance to the crop inventory.
机译:当前研究的主要目的是研究使用c波段混合极化Risat-1 SAR图像监测作物并区分其他土地覆盖目标的分解方法的潜力。本文选择了印度的Burdwan和Bharatpur这两个研究领域来分析各种现有的分解方法。实验中采用了Risat-1混合极化SAR单视复合物(SLC)数据,采用升序观测模式,分别于2014年12月22日和2016年8月3日从Burdwan和Bharatpur地区的部分地区获取。斯托克斯经典参数G_0,G_1,G_2和G_3是从混合SAR图像导出的,以进行进一步分析。根据这些斯托克斯参数,可以计算出相对相位,偏振度,取向,椭圆率和偏振角。此外,还执行了四种分解技术,即m-δ,m-α,m-x和修饰的m-x,并以奇数反弹,偶数反弹和体积分量的形式表示,用于监测研究区域内的农作物和周围土地覆盖目标。从监督分类的基础上观察到初步结果,该分解方法用于识别研究地点的各种作物,贫瘠土地,城市地区和水体。已经表明,与奇数和偶数反弹分量相比,所有分解中的体积分量都被高估了。发现Risat-1混合SAR数据对于区分各种土地覆盖目标更合适,方便且具有成本效益,而无云光学数据是作物库存的主要障碍。

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