Detecting and classifying objects from satellite images are crucial for many applications, ranging from marine monitoring to land planning, ecology to warfare, etc. Spatial and temporal information-rich satellite images are exploited in a variety of manners to solve many real-world remote sensing problems. Satellite image classification has many associated challenges. These challenges include data availability, the quality of data, the quantity of data, and data distribution. These challenges make the analysis of satellite images more challenging. A convolutional neural network architecture with a scaling method is proposed for the classification of satellite images. The scaling method can evenly scale all dimensions of depth, width, and resolution using a compound coefficient. It can be used as a preliminary task in urban planning, satellite surveillance, monitoring, etc. It can also be helpful in geo-information and maritime monitoring systems. The proposed methodology is based on an end-to-end, scalable satellite image interpretation. It uses spatial information from satellite images to categorize these into four categories. The proposed method gives encouraging and promising results on a challenging dataset with a high inter-class similarity and intra-class variation. The proposed method shows 99.64% accuracy on the RSI-CB256 dataset.