Delving into deep representations for remote sensing image retrieval
Fan Hu, Xinyi Tong, Gui-Song Xia, Liangpei Zhang · 2016
Deep features extracted from Convolutional Neural Networks (CNNs) have proved to have strong ability to transfer for various visual recognition tasks, such as image classification, object detection, fine-grained recognition, and image instance-level retrieval. In this paper, we focus on the issue of high-resolution remote sensing (HRRS) image retrieval and take advantage of CNN representations to solve this significant problem. In order to explore how domain-specific fine-tuning would impact the property of CNNs, we retrain several representative off-the-shelf CNN models with a public HRRS dataset RSSCN7, which contains limited annotated satellite images, and perform retrieval on other two standard HRRS datasets, RS19 and UCM. Then we introduce multi-scale concatenation and multi-patch pooling methods for further performance improvement. Our experimental results indicate that fine-tuning is effective to make progress on CNN transferability and provide remarkable accuracy that outperforms previous state-of-the-art methods.