Comparative Study of Pretrained Models for Remote Sensing Image Classification

Abstract

Remote Sensing (RS) image classification, particularly involving Earth Observation (EO) satellite data, presents significant challenges due to the complexity and variety of image content. This study addresses these challenges by evaluating the performance of three advanced deep learning models—DenseNet121, ResNet50, and EfficientNetB7—on the UC Merced Land Use (UCM) dataset. By leveraging pre-trained Convolutional Neural Networks (CNNs) through transfer learning, our approach effectively mitigates the issue of limited labelled data and enhances the accuracy of classification in high-resolution aerial imagery. This paper provides an in-depth study of said models, emphasizing their accuracy, precision, recall, and computing efficiency in the classification of land use domains. The findings provide insightful information about how well these various CNN architectures perform in classifying remote sensing images and lay the groundwork for further deep learning-based land use categorization research