Abstract
Remote sensing image retrieval (RSIR) is the process of searching for identical areas by investigating the similarities between a query image and the database images. RSIR is a challenging task owing to the time difference, viewpoint, and coverage area depending on the shooting circumstance, resulting in variations in the image contents. In this paper, we propose a novel method based on a coarse-to-fine strategy, which makes a deep network more robust to the variations in remote sensing images. Moreover, we propose a new triangular loss function to consider the whole relation within the tuple. This loss function improves the retrieval performance and demonstrates better performance in terms of learning the detailed information in complex remote sensing images. To verify our methods, we experimented with the Google Earth South Korea dataset, which contains 40,000 images, using the evaluation metric Recall@n. In all experiments, we obtained better performance results than those of the existing retrieval training methods. Our source code and Google Earth South Korea dataset are available online.
Highlights
As high-resolution remote sensing (RS) images have become accessible owing to the advancement of Internet technology and remote sensors, there is a growing interest in managing large databases for using in various domains, such as military, navigation, and delivery services
We focus on the task of retrieving the remote sensing images from an input aerial image
We propose a framework based on deep metric learning to search for query images that are identical to the region of a database image
Summary
As high-resolution remote sensing (RS) images have become accessible owing to the advancement of Internet technology and remote sensors, there is a growing interest in managing large databases for using in various domains, such as military, navigation, and delivery services. An RSIR system consists of a feature extraction and a similarity comparison unit, both of which are important parts to determining the success or failure of systems closely related to each other. Feature extraction is a major step in the retrieval process of RS images, as it summarizes images into high-dimensional features. The quality of the extracted features representing the images determines the success or failure of the system. The similarities between the summarized high-dimensional features are compared to determine the similarities between images. The similarities between the features are expressed as the Euclidean distances between features. The images with the smallest Euclidean distance values are determined to be the most similar images.
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