Enhanced Satellite Image Classification through Deep Learning using CNN’s

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.