Study on High-resolution Remote Sensing Image Scene Classification Using Transfer Learning

Abstract

Remote sensing image classification (RSIC) has been increasingly concerned and becoming a challenging task. Recently, deep convolutional neural networks (DCNN) offer the effective classification method include the capacity to handle high-dimensional data and to distinguish classes with very complex characteristics on the remote sensing community. However, the focus of these methods is on publicly available data sets in the field of remote sensing, there are few studies on RSIC composed of different benchmark datasets, which the complexity, diversity, and similarity of data greatly increase the difficulty of classification. In this paper, we reconstructed and selected one new dataset from two standard benchmark remote sensing datasets: UC Merged Land-Use and NWPU-RESISC45. We utilize three transfer learning frameworks to extract the high-level feature map and feed feature information into the proposed model for partial and full fine-tuning. Data augmentation technology is used to increase the number of training samples and dropout strategies to prevent overfitting. The experimental results demonstrate that the proposed methodology achieved remarkable performance in scene classification of overall accuracy: 90.1%,91.0%,93.3 with VggNet, DesNet, InceptionNet, respectively.