  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%.
