Enhanced Satellite Image Classification through Deep Learning using CNN’s

Enhanced Satellite Image Classification through Deep Learning using CNN’s

G.Sowmya1, P.Harsha Vardhan2, B.Lokesh3, E.Jeevan Kumar4, S.Neeraja5

[1-4] B. Tech Student  , 5Assistant Professor  ,LIET

     [1,2,3,4,5]Computer Science and Systems Engineering, Lendi Institute of Engineering and Technology, Viziangaram.

---------------------------------------------------------------------***---------------------------------------------------------------------

ABSTRACT

Satellite image classification plays a crucial role in applications such as land cover mapping, urban planning, and environmental monitoring. This project focuses on the development of SpectrumNet, a custom Convolutional Neural Network (CNN) designed to classify satellite images from the EuroSAT dataset into ten distinct land-use categories. The primary goal is to improve the accuracy and efficiency of satellite image classification through the implementation of advanced preprocessing techniques, optimized training strategies, and multiple network configurations. By utilizing deep learning methodologies, SpectrumNet aims to provide reliable insights for real-world applications in remote sensing, geospatial analysis, and environmental studies. The project strives to enhance the ability to analyze and interpret large-scale satellite image data, offering valuable information for decision-making in various fields.

Key Words: Deep Learning, Convolutional Neural Networks, EuroSAT, SpectrumNet, Geospatial Analysis, Land Cover Mapping, Environmental Monitoring, Urban Planning.