Object detection in remote sensing images based on deep transfer learning

Abstract

Object detection is a basic part in remote sensing image processing. At present, it is more common to conduct the topic based on deep learning, however the volume of remote sensing images has become a limitation. In order to solve the problem of small sample of remote sensing image, transfer learning is combined with deep learning in the research. First, the detection problem is caused by insufficient data, such as over-fitting, which is solved by model-based transfer learning. The structure of models and parameters obtained based on natural images are transferred to the detection task in remote sensing target domain. In addition, it is usually assumed that the distribution of training data and the testing data are the same in detection, but this is not the case. Therefore, how to improve the robustness of training models and widen the scope of application should be taken into consideration. In the research, Domain Adaptation Faster R-CNN (DA Faster R-CNN) algorithm is proposed for detecting aircraft in remote sensing images. Two domain adaptation structures are designed and selected as the criterion of similarity measurement between domains. Adversarial training is applied to alleviate the domain shift. Finally, the effectiveness of the algorithm is certified in the low brightness experiment. DA Faster R-CNN detection algorithm improves the accuracy of the original algorithm for low quality images. It is worth noting that the DA Faster R-CNN algorithm is a kind of unsupervised transfer learning method for remote sensing object detection.