Unsupervised Balanced Hash Codes Learning With Multichannel Feature Fusion

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Yaxiong Chen Dongjie Zhao Xiongbo Lu Shengwu Xiong Huangting Wang
Abstract	Unsupervised hashingalgorithms are widely used in large-scale remote sensing images (RSIs) retrieval task. However, existing RSI retrieval algorithms fail to capture the multichannel characteristic of multispectral RSIs and the balanced property of hash codes, which lead the poor performance of RSI retrieval. To tackle these issues, we develop an unsupervised hashing algorithm, namely, variational autoencoder balanced hashing (VABH), to leverage multichannel feature fusion and multiscale context information to perform RSI retrieval task. First, multichannel feature fusion module is designed to extract RSI feature information by leveraging the multichannel properties of multispectral RSI. Second, multiscale learning module is developed to learn the multiscale context information of RSIs. Finally, a novel objective function is designed to capture the discrimination and balanced property of hash codes in the hashing learning process. Comprehensive experiments on diverse benchmark have well demonstrated the reasonableness and effectiveness of the proposed VABH algorithm.
Related Links	https://ieeexplore.ieee.org/document/9743294/
e-ISSN	21511535
DOI	10.1109/JSTARS.2022.3162251
Journal	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume Number	15
Language	English
Publisher	IEEE
Publisher Date	2022-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Ocean Engineering Geophysics. Cosmic Physics Deep Hash Codes Multichannel Feature Fusion Multiscale Context Information Unsupervised Hashing Learning
Content Type	Text
Resource Type	Article