Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

• Published in: Remote sensing (Basel, Switzerland) Vol. 9; no. 3; p. 225
• Main Authors: Wang, Jie, Luo, Chang, Huang, Hanqiao, Zhao, Huizhen, Wang, Shiqiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2017
• Subjects: Artificial neural networks; Classification; Computer vision; convolutional neural network; Datasets; deep learning; Feature extraction; general feature; Neural networks; principle component analysis; Quaternions; remote scene classification; Remote sensing; Spatial data
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs9030225

Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet) to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial “distance” of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral “distance” between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pretrained deep CNNs to other remote sensing tasks.