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IJSTR >> Volume 10 - Issue 6, June 2021 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Classification Of Wheat Crop Using Remotely Sensed Multi-Spectralplanet-Scope Temporal Data

[Full Text]

 

AUTHOR(S)

Awab Ur Rashid Durrani, Arbab Masood Ahmad

 

KEYWORDS

Remote sensing, Classification, Temporal, Artificial neural network, Support vectormachine, Minimum distance, Planetscope.

 

ABSTRACT

Agriculture plays a vital role in the economies of developing countries and provide the mainsource of food, income and employment for general public. Monitoring and assessment of the crop yieldis a crucial task and is critical in ensuring good agricultural management. We propose the monitoring andclassification of wheat crops through remote sensing by utilizing satellite imagery. In our research work,we have utilized multi-spectral imagery of Planet-Scope satellite for the classification of wheat crop. Theimagery used is a temporal stack of remotely sensed imageries obtained on various dates with reference tothe phenological cycle of wheat.We employ three different machine learning classifiers i.e., Artificial NeuralNetwork (ANN), Support Vector Machine (SVM) and Minimum Distance (MD) classifier for the wheatcrop classification. Confusion matrix and Kappa Coefficient(Kappa Coefficient) analyzes the performanceof these three classifiers. The results obtained shows that ANN with an overall accuracy of 98:7031%and Kappa Coefficient equivalent to 0:9825 outperforms the SVM and MD classifiers having the overallaccuracy of 85:2005% and 73:1604% and Kappa Coefficient values of 0:8097 and 0:6455, respectively.

 

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