Sathyanarayana, Natya
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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Mysore sentinel-2: deep learning for image classification with optimizer exploration Sathyanarayana, Natya; Singh, Seema

Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp647-657

Abstract

The classification of Sentinel-2 image is presented in this work using a tile based methodology. The Mysore district of India's Karnataka state serves as the subject region of this research. By tiling Sentinel-2 images, we were able to construct a distinct dataset and get approximately 3,000 training samples for the five classes. These images are manually labelled and geo-referenced. Three different optimizers were employed in a thorough analysis with deep learning models such as ResNet50, MobileNetV2, ShuffleNet, and VGG16 to achieve better performance metrics. With a classification accuracy of 98.1% on RESNet50 using ADAM surpassed the others. This facilitates investigating various geographical data analytics applications of the study region.