In this paper, convolutional neural networks (CNNs) is employed for remote-sensing scene classification, which fully utilizes the semantic features extracted from the images while ignoring some traditional features. Consider the limited labeled samples, CaffeNet model as the pre-trained architecture is adopted. By fine-tuning the pre-trained models, the proposed method is expected to be robust and efficient. Its performance is evaluated with two remote-sensing scene datasets. From the experimental results, the proposed CNN-based scene classification method does provide more excellent performance and be superior to several state-of-the-art methods.
['Zhengzheng Fang', 'Wei Li', 'Jinyi Zou', 'Qian Du']
Using CNN-based high-level features for remote sensing scene classification