Hashing Nets for Hashing: A Quantized Deep Learning to Hash Framework for Remote Sensing Image Retrieval

Li, Peng, Han, Lirong, Tao, Xuanwen, Zhang, Xiaoyu, Grecos, Christos, Plaza, Antonio and Ren, Peng (2020) Hashing Nets for Hashing: A Quantized Deep Learning to Hash Framework for Remote Sensing Image Retrieval. IEEE Transactions on Geoscience and Remote Sensing, 58 (10). pp. 7331-7345. ISSN 1558-0644

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Abstract

Fast and accurate remote sensing image retrieval from large data archives has been an important research topic in the remote sensing research literature. Recently, hashing-based remote sensing image retrieval has attracted extreme attention because of its efficient search capabilities. Especially, deep remote sensing image hashing algorithms have been developed based on convolutional neural networks (CNNs) and have shown effective retrieval performance. However, implementing a deep hashing network tends to be highly expensive in terms of storage space and computing resources to be suitable for on-orbit remote sensing image retrieval, which usually operates on resource-limited devices such as satellites and unmanned aerial vehicles (UAVs). To address this limitation, we propose to hash a deep network that in turn hashes remote sensing images. Specifically, we develop a quantized deep learning to hash (QDLH) framework for large-scale remote sensing image retrieval. The weights and activation functions in the QDLH framework are binarized to low-bit representations, which require comparatively much less storage space and computing resources. The QDLH results in a lightweight deep neural network for effective remote sensing image hashing. We conduct extensive experiments on two public remote sensing image data sets by incorporating several state-of-the-art network architectures into our QDLH methodology for remote sensing image hashing. The experimental results demonstrate that the proposed QDLH is effective in saving hardware resources in terms of both storage and computation. Moreover, superior remote sensing image retrieval performance is also achieved by our QDLH, compared with state-of-the-art deep remote sensing image hashing methods.

Item Type:	Article
Uncontrolled Keywords:	Remote sensing; Image retrieval; Task analysis; Neurons; Satellites; Machine learning; Computational modeling; Class intensive; deep hashing; quantized deep network; remote sensing images retrieval
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QE Geology Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	11 Aug 2023 10:34
Last Modified:	11 Aug 2023 10:34
URI:	https://norma.ncirl.ie/id/eprint/6777

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