Scalable Deep Learning for Categorization of Satellite Images

Satellite imagery serves a crucial role in diverse applications like disaster response, law enforcement, and environmental monitoring. These tasks often require manual identification of objects and structures within the images. However, due to the extensive geographic regions involved and a limited number of analysts available, there’s a growing need for automation. Traditional methods of object detection and classification are proving insufficient and unreliable for addressing this challenge. This is where deep learning comes into play—a collection of machine learning techniques that holds the potential to automate these tasks effectively. In particular, convolutional neural networks (CNNs), a subset of deep learning, have shown remarkable promise in understanding images. This study focuses on applying CNNs to the complex task of recognizing various objects and facilities in high-resolution, multi-spectral satellite images. The research introduces a deep learning system that takes advantage of both satellite metadata and image features to accurately classify the dataset into different categories. The implementation of this system is carried out using Python, leveraging the Keras and TensorFlow libraries. Comparative analysis against existing systems reveals a significant enhancement in overall accuracy offered by this approach.