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

Content Provider	Springer Nature Link
Author	Pandey, Abhishek Prasad, R. Jha, Sunil Kr.
Copyright Year	2010
Abstract	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.
Starting Page	141
Ending Page	145
Page Count	5
File Format	PDF
ISSN	10683674
Journal	Russian Agricultural Sciences
Volume Number	36
Issue Number	2
e-ISSN	19348037
Language	English
Publisher	Allerton Press, Inc.
Publisher Date	2010-06-26
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Soil surface roughness scattering coefficient remote sensing support vector machine Plant Biochemistry Agriculture
Content Type	Text
Resource Type	Article

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