Unsupervised Balanced Hash Codes Learning With Multichannel Feature Fusion

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.

Keywords

• Deep hash codes
• multichannel feature fusion
• multiscale context information
• unsupervised hashing learning