SATELLITE IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORK

Satellite image analysis plays a pivotal role in monitoring and understanding various Earth processes, including environmental changes, urban expansion, and natural disasters. In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for image processing tasks, making them highly effective for satellite image analysis. This project explores the application of CNNs to classify, detect, and segment features in satellite imagery. By leveraging CNN architectures, the project aims to improve the accuracy and efficiency of extracting meaningful patterns and insights from high-dimensional satellite data. Our approach includes training CNN models on labeled datasets, tuning hyperparameters, and evaluating performance across different landscape types. The results demonstrate the potential of CNN-based models to support real-time satellite image analysis, providing valuable insights for fields such as environmental monitoring, agriculture, and disaster management.