Supervised Contrastive Learning-Based Deep Hash Retrieval for Remote Sensing Image

Mengluan Huang, Le Dong, Weisheng Dong, Guangming Shi

Abstract: With the development of remote sensing technology, the earth observation data shows a blowout growth. How to quickly and accurately retrieve the target content from the massive data has become a noteworthy task. Recently, the methods based on convolutional neural networks (CNN) have been far ahead in remote sensing retrieval. However, due to the diversity of sources acquired, remote sensing images with the same semantic information may have great visual differences. The pre-trained CNN is not always able to successfully extract representative and distinguishing features to recognize the target content. To address this problem, a supervised contrastive learning-based deep hash retrieval method (SCLDHR) is introduced in this paper, which effectively uses label information to gather image features belonging to the same class and separate image features of different classes in the embedding space. Furthermore, a quantization function in the hash space is designed to improve hashing quality. The experimental results conducted on three datasets show that SCLDHR can achieve competitive retrieval performance compared with state-of-the-art methods.