Satellite Image Detection Using Image Processing

Abstract

A technique for locating objects and patterns in satellite-captured photos is called satellite image detection. Convolutional Neural Networks (CNNs), a kind of deep learning model, are used in this project to increase the accuracy and efficiency of this procedure. CNNs can handle the complexity of high-resolution satellite photos and are very good at processing image data. After scaling and normalizing the photos, CNNs are used by the system to identify and categorize elements including vegetation, buildings, and bodies of water. The model was trained using a dataset of diverse satellite photos, yielding dependable outcomes and excellent accuracy. Disaster management, crop monitoring, urban planning, and environmental preservation are just a few of the many useful applications for this technology. This method demonstrates how deep learning can enhance satellite picture processing for practical applications, even when obstacles like high image sizes still exist.