Enhanced Satellite Image Classification Using Deep Convolutional Neural Networks with Data Augmentation

Abstract

Satellite image categorization is a crucial component of applications such as land-use analysis, agricultural monitoring, environmental evaluation, and disaster response. This article presents a deep learning strategy utilizing Convolutional Neural Networks (CNNs) for the efficient categorization of satellite data. The suggested system incorporates effective data augmentation methods, contemporary CNN architectures, and refined training methodologies to improve accuracy and generalization capabilities. An experimental assessment on a benchmark dataset of satellite images demonstrates that our model exhibits robust performance, with a validation accuracy of 98.93% and elevated precision, recall, and F1 scores. The created model exhibits significant improvements in accuracy and computing efficiency relative to traditional classification methods. The results indicate that this methodology is exceptionally well-suited for satellite image classification and can be further enhanced through techniques such as transfer learning, hybrid deep learning architectures, and multimodal feature integration to address more complex real-world scenarios.

Keywords: CNN, data augmentation, model optimization, classification accuracy, transfer learning.