Disaster relief, police work, and environmental checks rely heavily on satellite imagery. Some users need human assistance manually identifying facilities and items in the photos. Due to the significant regions that need to be searched and the scarcity of available analysts, automation is crucial. Yet, standard object identification and classification techniques must improve accuracy to solve the problem. A set of machine learning techniques called “deep learning” has shown promise for automating these operations. It has had success using convolutional neural networks to comprehend images. Using high-resolution, multi-spectral satellite images, we apply them to the issue of object and facility identification in this work. We outline a deep-learning object classification system. We use the Satellite Image Classification Dataset-RSI- CB256 for this study. This dataset has four classifications combined using Google Maps snapshots and sensors. Suggested hybrid model in this paper gives an accuracy of 98.96%.