Impact analysis of convolutional neural network in classification of satellite imagery

The classification of satellite images is crucial for information extraction and analysis. It is a method for categorizing images based on their features. The classification process involves identifying different details along with satellite imagery patterns. Any decision made in a remote sensing study is primarily determined by how well the method of classification is performing. The process of classifying data or images is difficult. Numerous elements, such as the mixed pixel issue, can have an impact on this process. In this research paper, convolutional neural networks that are used to create remote sensing-based image classification. To determine the precision and effectiveness of the proposed deep neural network model, comparison analysis has been conducted between the proposed model and various other CNN architectures, including LeNet, AlexNet, VGGNet, and ZfNet.