Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, for the task of remote scene classification, there are no sufficient images to train a very deep CNN from scratch. Instead, transferring successful pre-trained deep CNNs to remote sensing tasks provides an effective solution. Firstly, from the viewpoint of generalization power, we try to find whether deep CNNs need to be deep when applied for remote scene classification. Then, the pre-trained deep CNNs with fixed parameters are transferred for remote scene classification, which solve the problem of time-consuming and parameters over-fitting at the same time. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that transferred deep CNNs can achieve state-of-the-art results in unsupervised setting. This chapter also provides baseline for applying deep CNNs to other remote sensing tasks.
Part of the book: Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure