Classification of two different rough soil surfaces by using microwave X-band data through support vector machine (SVM)

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• 作者：Abhishek Pandey (1)
  R. Prasad (1)
  Sunil Kr. Jha (2)
  
• 关键词：Soil surface roughness ; scattering coefficient ; remote sensing ; support vector machine
• 刊名：Russian Agricultural Sciences
• 出版年：2010
• 出版时间：April 2010
• 年：2010
• 卷：36
• 期：2
• 页码：141-145
• 全文大小：259KB
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• 作者单位：Abhishek Pandey (1)
  R. Prasad (1)
  Sunil Kr. Jha (2)
  
  1. Department of Applied Physics, Institute of Technology, Banaras Hindu University, Varanasi, 221005, Uttar Pradesh, India
  2. Department of Physic, Banaras Hindu University, Varanasi- 221005, Uttar Pradesh, India
  

文摘

Support vector machine (SVM) model is employed and tested for the soil surface roughness classification. SVM is calibrated (trained) and tested with the experimentally obtained data. The experimentally data is obtained by using X-band (9.5 GHz) scatterometer for two soil surface roughness 3.78 cm and 1.83 cm at constant soil surface moisture equal to 22.80%. The measurement of the scattering coefficient was carried out over a range of incidence angle from 20° to 70° at the step of 5° for both the HH and vv polarization. The performance of the SVM model is evaluated from the outcome classification result on trained data set and test data set. Radial Gaussian kernel function results 100% correct Classification and identification of soil surface roughness both in training and validation phase. SVM is a proficient technique for soil surface roughness classification by such experimentation and have numerous of advantages over artificial neural network (ANN) based approaches and other theoretical approaches as its less complexity and less time consumption ability.