Optimization for Supervised Satellite Images Classification


Authors : Vasamsetti Akhileswar; Munganty Rahul; Devendra Phani Kumar

Volume/Issue : Volume 6 - 2021, Issue 3 - March

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/320GTfw

Remote sensing is a method of detecting and inspecting the characteristics of objects. It uses magnetic energy within the range of heat, light, and radio waves. Very different applications that use remote sensing are agribusiness, disaster dashboards, water management, etc. The strategy of creating thematic maps based on remote imagination is called imageclassification. For at least one phantom computer, the extended number usually represents spectral information. Used to classify images. This data is used to classify individual pixels on the spectrum. Used for classification. This article introduces a classifier specifically controlled by a minimum distance, maximum likelihood, and parallelepiped. By using PSO technology (particle swarm enhancement), we can achieve better results. Constant letters and general precision. Sentences: classification, confusion nets, letter coefficients, total accuracy, parallel classifiers, remote sensing, improved methods.

Keywords : Classification, Confusion Matrix, Kappa Coefficient, Overall Accuracy, Parallelepiped Classifier, Remote Sensing, Optimization Techniques.

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