A MobileNetV2 model of transfer learning is employed for remote sensing image classification
DOI:
https://doi.org/10.56028/aetr.10.1.596.2024Keywords:
Scene classification; Convolutional neural network; MobileNetV2; Transfer learning.Abstract
Remote sensing image classification is an important and complicated problem in deep learning field. In order to achieve better classification effect, predecessors have proposed different kinds of models. A transfer learning-based MobileNetV2 model is suggested in this paper. First, we take UC-Merced dataset as input and introduce lightweight network MobileNetV2 for scene identification. Secondly, we combined the transfer learning training method and pre-trained the MobileNetV2 model to realize the high-performance classification of deeper remote sensing images. The UC- Merced dataset was classified with a model that achieved 91.43% accuracy,91.00% kappa index, and 91.50% F1-score. These results demonstrate the model's impressive performance in remote sensing image classification and its potential for practical scene identification applications.