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DRL-GAN: dual-stream representation learning GAN for low-resolution image classification in UAV applications.

Xi, Yue; Jia, Wenjing; Zheng, Jiangbin; Fan, Xiaochen; Xie, Yefan; Ren, Jinchang; He, Xiangjian

Authors

Yue Xi

Wenjing Jia

Jiangbin Zheng

Xiaochen Fan

Yefan Xie

Jinchang Ren

Xiangjian He



Abstract

Identifying tiny objects from extremely low resolution (LR) UAV-based remote sensing images is generally considered as a very challenging task, because of very limited information in the object areas. In recent years, there have been very limited attempts to approach this problem. These attempts intend to deal with LR image classification by enhancing either the poor image quality or image representations. In this paper, we argue that the performance improvement in LR image classification is affected by the inconsistency of the information loss and learning priority on Low-Frequency (LF) components and High-Frequency (HF) components. To address this LF-HF inconsistency problem, we propose a Dual-Stream Representation Learning Generative Adversarial Network (DRL-GAN).The core idea is to produce super image representations optimal for LR recognition by simultaneously recovering the missing information in LF and HF components, respectively, under the guidance of high-resolution (HR) images. We evaluate the performance of DRL-GAN on the challenging task of LR image classification. A comparison of the experimental results on the LR benchmark, namely HRSC and CIFAR-10, and our newly collected “WIDER-SHIP” dataset demonstrates the effectiveness of our DRL-GAN, which significantly improves the classification performance, with up to 10% gain on average.

Citation

XI, Y., JIA, W., ZHENG, J., FAN, X., XIE, Y., REN, J. and HE, X. [2020]. DRL-GAN: dual-stream representation learning GAN for low-resolution image classification in UAV applications. IEEE Journal of selected topics in applied earth observations and remote sensing [online], Early Access. Available from: https://doi.org/10.1109/JSTARS.2020.3043109

Journal Article Type Article
Acceptance Date Nov 25, 2020
Online Publication Date Dec 8, 2020
Deposit Date Jan 7, 2021
Publicly Available Date Jan 7, 2021
Journal IEEE Journal of selected topics in applied earth observations and remote sensing
Print ISSN 1939-1404
Electronic ISSN 2151-1535
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/JSTARS.2020.3043109
Keywords UAV-based remote sensing; Generative adversarial networks; Low resolution image classification; Convolutional neural networks; Representation learning
Public URL https://rgu-repository.worktribe.com/output/1085085

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