Improved Intelligence Gathering for Satellite Images of Varying Resolutions Using Deep Learning Techniques

Satellite is a piece of equipment that has been sent into orbit in order to gather data or function as a component of a communication system. Satellite imagery plays a crucial role in numerous applications, including environmental monitoring, disaster response, urban planning, and military surveillance. However, effectively extracting meaningful information from satellite images of varying resolutions has remained a challenging task. Deep learning techniques leverages on the enhancement intelligence gathering from satellite images with varying resolutions. This study aims to investigate how deep learning methods may improve intelligence collection using satellite photos with various resolutions. The objectives are concerned with showing how well deep learning works for varying resolutions of satellite images in five different applications as; object detection and classification, tracking changes, classifying and mapping land cover, disaster management and response, military and security applications. In achieving this, we employ deep learning architectures (methods) as the convolutional neural network capable of automatically learning intricate features from the input images and the results obtained from the study indicate that deep learning-based framework significantly improves intelligence gathering from satellite images of varying resolutions. In conclusion, the successful development and implementation of a CNN model (an innovative and effective approach to enhance intelligence gathering from satellite images with varying resolutions) for improved intelligence gathering from satellite images, have led to transformative advancements across multiple domains, and solutions to the challenges associated with diverse image characteristics and framework which was able to exhibits and improved accuracy and scalability.