Deep Learning for Satellite Image Analysis in Space Exploration

Satellite images are essential for space exploration because they help to determine the nature of planetary bodies, search for key materials, track changing weather conditions, and find objects. However, higher levels of complexity and the amount of data shared become the most significant challenges. This review looks at how such issues have been deal with using deep learning, which comprises CNNs, RNNs, GANs, and Transformers for operations including image classification, object detection, and segmentation. Some major applications of remote sensing data include planetary mapping, exploration, climatic changes, and where. However, problems such as data quality, computational cost, and ethical issues are still cases in point. Prospects in California, such as multimodal data integration, real-time processing or quantum processing, are likely to produce more growth in this field. This review aims to give an overview of the breakthrough that deep learning has brought and is bringing to satellite image analysis.