Transfer Deep Learning for Remote Sensing Datasets: A Comparison Study

Abstract

Remote sensing is also benefiting from the quick development of deep learning algorithms for image analysis and classification tasks. In this paper, we evaluate the classification performance of a well-known Convolutional Neural Network (CNN) models, such as ResNet50, using a transfer learning approach. We compare the performance when using vector-features acquired from general purpose data, such as the ImageNet [1], versus remote sensing data like BigEarthNet [2], UCMerced [3], RESISC45 [4] and So2Sat [5]. The results show that the model pre-trained on RESISC-45 data achieved the highest accuracy when classifying the Eurosat [6] testing dataset. This was followed by the model pre-trained on Imagenet with 95.94% and BigEarthNet with 95.93%. When presented with diverse remote sensing data, the classification improved in regards to large quantities of general-purpose data. The experiments carried out also show, that multi modal (co-registered synthetic aperture radar and multispectral) did not increase the classification rate with respect to using only multispectral data. The source codes of this work are available for reproducible research at https://github.com/itzahs/CNN-RS.