LAUSR

With the development of multimedia technology, the application of panoramic video is emerging, and there are specific application scenarios in the field of drones. Remote sensing images have tremendous applications in data acquisition and processing in the panoramic maps used to construct the panoramic videos. Efficient remote sensing image retrieval can greatly improve the efficiency of image utilization and get a fast response in the construction of panoramic video. Thus, how to achieve fast and efficient remote sensing image retrieval becomes increasingly important. Scholars have explored a number of ways to address this problem, from traditional manual feature-based retrieval methods to deep hash retrieval methods that today can cope with large data volumes. The hash method is a way to encode high-dimensional data into a set of binary codes while maintaining similarity between images. In this paper, we propose an end-to-end ternary label depth hashing method that is capable of both feature learning and hash coding processes in one-stage. The experimental results show that the algorithm has high accuracy and good retrieval effect.