Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing

Author:

Tang XuORCID,Liu Chao,Ma Jingjing,Zhang XiangrongORCID,Liu Fang,Jiao Licheng

Abstract

Remote sensing image retrieval (RSIR), a superior content organization technique, plays an important role in the remote sensing (RS) community. With the number of RS images increases explosively, not only the retrieval precision but also the retrieval efficiency is emphasized in the large-scale RSIR scenario. Therefore, the approximate nearest neighborhood (ANN) search attracts the researchers’ attention increasingly. In this paper, we propose a new hash learning method, named semi-supervised deep adversarial hashing (SDAH), to accomplish the ANN for the large-scale RSIR task. The assumption of our model is that the RS images have been represented by the proper visual features. First, a residual auto-encoder (RAE) is developed to generate the class variable and hash code. Second, two multi-layer networks are constructed to regularize the obtained latent vectors using the prior distribution. These two modules mentioned are integrated under the generator adversarial framework. Through the minimax learning, the class variable would be a one-hot-like vector while the hash code would be the binary-like vector. Finally, a specific hashing function is formulated to enhance the quality of the generated hash code. The effectiveness of the hash codes learned by our SDAH model was proved by the positive experimental results counted on three public RS image archives. Compared with the existing hash learning methods, the proposed method reaches improved performance.

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Cited by 33 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep global semantic structure-preserving hashing via corrective triplet loss for remote sensing image retrieval;Expert Systems with Applications;2024-03

2. Hash-Based Remote Sensing Image Retrieval;IEEE Transactions on Geoscience and Remote Sensing;2024

3. Attention-based localized hash retrieval for large-scale remote sensing images using deep feature splitting strategy;Journal of Applied Remote Sensing;2023-07-31

4. Adaptive hash code balancing for remote sensing image retrieval;International Journal of Remote Sensing;2023-01-17

5. Encoding Human Visual Perception Into Deep Hashing for Aerial Image Classification;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2023

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