Remote sensing image scene recognition using MobileNet

Abstract. Remote sensing image scene recognition plays a pivotal role in various applications, including environmental monitoring, disaster response, urban planning, precision agriculture, and aids in resource management and policy formulation. However, utilizing established convolutional neural networks(CNNs) models like AlexNet and VGG9 for this task can be computationally intensive and time-consuming due to their extensive parameter requirements. This dissertation introduces a MobileNet-based CNN optimized for remote sensing image scene recognition. This lightweight model significantly reduces computational load and model size without compromising accuracy, thereby enhancing efficiency. Empirical results on the NWPU45 dataset demonstrate MobileNet's superiority, achieving an accuracy of 91.16%, a Kappa coefficient of 90.96%, and an F1 score of 91.16% on the test set. Moreover, MobileNet's compact architecture, with merely 3.2531 million parameters and 587.9342 million FLOPs, underscores its efficiency and makes it a promising candidate for practical deployment in remote sensing applications. The findings suggest that MobileNet not only addresses the challenge of computational intensity but also opens new avenues for advancing scene recognition technology in the field of remote sensing.