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

Xu Tang,Jingjing Ma,Xiangrong Zhang,Chao Liu,Licheng Jiao,Fang Liu

doi:10.3390/rs11172055

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.

Highlights

With the development of Earth observation (EO) techniques, remote sensing (RS) has entered the big data era
Among the diverse approximate nearest neighborhood (ANN) methods, hashing is a successful solution for many applications, which focuses on mapping the images from the original feature space to the hash space
Under the paradigm of generative adversarial learning, a new semi-supervised deep hashing method named supervised deep adversarial hashing (SDAH) is presented in this paper

Summary

Introduction

With the development of Earth observation (EO) techniques, remote sensing (RS) has entered the big data era. The number of RS images collected by the EO satellites every day is increased explosively. How to manage these massive amounts of RS images in the content level becomes an open and tough task in the RS community [1]. As a useful management tool, remote sensing image retrieval (RSIR) is always adopted to organize the RS images according to their contents [2]. RSIR is a complicated technology that consists of a series of image processing methods [3], such as feature extraction, similarity matching, etc. It is a comprehensive technology that covers a large number of techniques [4], such as feature representation, metric learning, image annotation, image caption, etc

Methods

Results

Conclusion