Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study

TL;DR

This study applies transfer learning with CNNs like VGG16 and WRNs to improve land use and land cover classification accuracy and efficiency using high-resolution remote sensing images, achieving state-of-the-art results.

Contribution

It demonstrates the effectiveness of transfer learning with pre-trained CNNs and optimization techniques for LULC classification, outperforming previous methods.

Findings

WRNs achieve 99.17% accuracy, surpassing previous results.

Transfer learning reduces training time and improves accuracy.

Optimization techniques enhance model performance and efficiency.

Abstract

Efficiently implementing remote sensing image classification with high spatial resolution imagery can provide a significant value in Land Use and Land Cover (LULC) classification. The new advances in remote sensing and deep learning technologies have facilitated the extraction of spatiotemporal information for LULC classification. Moreover, the diverse disciplines of science, including remote sensing, have utilised tremendous improvements in image classification by Convolutional Neural Networks (CNNs) with transfer learning. In this study, instead of training CNNs from scratch, the transfer learning is applied to fine-tune pre-trained networks Visual Geometry Group (VGG16) and Wide Residual Networks (WRNs), by replacing the final layer with additional layers, for LULC classification using the red-green-blue version of the EuroSAT dataset. Moreover, the performance and computational time are compared and optimised with techniques, such as early stopping, gradient clipping, adaptive learning rates, and data augmentation. The proposed approaches have addressed the limited-data problem, and very good accuracies are achieved. The results show that the proposed method based on the WRNs performs better than the previous best-stated results in terms of the computational efficiency and accuracy from 98.57% to 99.17%.