Enhancing Remote Sensing Image Classification: A Strategic Integration of Deep Learning Technique and Transfer Learning Approach

Image classification within the realm of remote sensing technology plays a vital role across various sectors, including military operations, geographical mapping, surveillance activities, and environmental monitoring. Procurement of sharp and accurate satellite imagery is presently uncomplicated. This investigation is geared toward implementing image classification through a sophisticated deep learning algorithm and assessing its prognostic capabilities. This study employs a comprehensive dataset comprising 5,631 satellite images, encompassing representations of cloud-covered regions, desert landscapes, verdant territories, and aquatic zones. The evolution and widespread adoption of deep neural networks, along with their methodologies, have experienced substantial growth. This research harnesses one of the most pivotal deep learning frameworks, Convolutional Neural Networks (CNNs). The efficacy of the CNN model deployed here is noteworthy, specifically employing the VGG-16 CNN architecture. The model exhibits significant performance improvement and achieves excellent validation accuracy. The evaluation findings demonstrate the model’s strong ability to categorize satellite imagery accurately, which strengthens its generalization capacity.