Boosting Small Ship Detection in Optical Remote Sensing Images via Image Super-Resolution

Linhao Li; Zhiqiang Zhou; Saijia Cui

doi:10.1109/CCDC52312.2021.9601674

Boosting Small Ship Detection in Optical Remote Sensing Images via Image Super-Resolution

Linhao Li, Zhiqiang Zhou^*, Saijia Cui

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Small ships in optical remote sensing images are hard to detect due to the lack of sufficient detail information. In this paper, we adopt the image super-resolution technology to solve this problem. Specifically, an effective super-resolution network is designed to generate clear super-resolution ship images from small blurry ones produced by the ship detector. Inspired by the idea of generative adversarial network (GAN), the super-resolution network is trained together with a discriminator network in an adversarial way, aiming at generating more realistic super-resolution images. Moreover, to eliminate false detections, the discriminator network is also used to distinguish ship and non-ship images via an additional classification branch. Experimental results demonstrate the effectiveness of the proposed method.

Original language	English
Title of host publication	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1508-1512
Number of pages	5
ISBN (Electronic)	9781665440899
DOIs	https://doi.org/10.1109/CCDC52312.2021.9601674
Publication status	Published - 2021
Event	33rd Chinese Control and Decision Conference, CCDC 2021 - Kunming, China Duration: 22 May 2021 → 24 May 2021

Publication series

Name	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

Conference

Conference	33rd Chinese Control and Decision Conference, CCDC 2021
Country/Territory	China
City	Kunming
Period	22/05/21 → 24/05/21

Keywords

Generative adversarial network
Image super-resolution
Ship detection

Access to Document

10.1109/CCDC52312.2021.9601674

Cite this

Li, L., Zhou, Z., & Cui, S. (2021). Boosting Small Ship Detection in Optical Remote Sensing Images via Image Super-Resolution. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021 (pp. 1508-1512). (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC52312.2021.9601674

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title = "Boosting Small Ship Detection in Optical Remote Sensing Images via Image Super-Resolution",

abstract = "Small ships in optical remote sensing images are hard to detect due to the lack of sufficient detail information. In this paper, we adopt the image super-resolution technology to solve this problem. Specifically, an effective super-resolution network is designed to generate clear super-resolution ship images from small blurry ones produced by the ship detector. Inspired by the idea of generative adversarial network (GAN), the super-resolution network is trained together with a discriminator network in an adversarial way, aiming at generating more realistic super-resolution images. Moreover, to eliminate false detections, the discriminator network is also used to distinguish ship and non-ship images via an additional classification branch. Experimental results demonstrate the effectiveness of the proposed method.",

keywords = "Generative adversarial network, Image super-resolution, Ship detection",

author = "Linhao Li and Zhiqiang Zhou and Saijia Cui",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd Chinese Control and Decision Conference, CCDC 2021 ; Conference date: 22-05-2021 Through 24-05-2021",

year = "2021",

doi = "10.1109/CCDC52312.2021.9601674",

language = "English",

series = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Li, L, Zhou, Z & Cui, S 2021, Boosting Small Ship Detection in Optical Remote Sensing Images via Image Super-Resolution. in Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1508-1512, 33rd Chinese Control and Decision Conference, CCDC 2021, Kunming, China, 22/05/21. https://doi.org/10.1109/CCDC52312.2021.9601674

Boosting Small Ship Detection in Optical Remote Sensing Images via Image Super-Resolution. / Li, Linhao; Zhou, Zhiqiang; Cui, Saijia.
Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1508-1512 (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Boosting Small Ship Detection in Optical Remote Sensing Images via Image Super-Resolution

AU - Li, Linhao

AU - Zhou, Zhiqiang

AU - Cui, Saijia

PY - 2021

Y1 - 2021

N2 - Small ships in optical remote sensing images are hard to detect due to the lack of sufficient detail information. In this paper, we adopt the image super-resolution technology to solve this problem. Specifically, an effective super-resolution network is designed to generate clear super-resolution ship images from small blurry ones produced by the ship detector. Inspired by the idea of generative adversarial network (GAN), the super-resolution network is trained together with a discriminator network in an adversarial way, aiming at generating more realistic super-resolution images. Moreover, to eliminate false detections, the discriminator network is also used to distinguish ship and non-ship images via an additional classification branch. Experimental results demonstrate the effectiveness of the proposed method.

AB - Small ships in optical remote sensing images are hard to detect due to the lack of sufficient detail information. In this paper, we adopt the image super-resolution technology to solve this problem. Specifically, an effective super-resolution network is designed to generate clear super-resolution ship images from small blurry ones produced by the ship detector. Inspired by the idea of generative adversarial network (GAN), the super-resolution network is trained together with a discriminator network in an adversarial way, aiming at generating more realistic super-resolution images. Moreover, to eliminate false detections, the discriminator network is also used to distinguish ship and non-ship images via an additional classification branch. Experimental results demonstrate the effectiveness of the proposed method.

KW - Generative adversarial network

KW - Image super-resolution

KW - Ship detection

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U2 - 10.1109/CCDC52312.2021.9601674

DO - 10.1109/CCDC52312.2021.9601674

M3 - Conference contribution

AN - SCOPUS:85125184752

T3 - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

SP - 1508

EP - 1512

BT - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 33rd Chinese Control and Decision Conference, CCDC 2021

Y2 - 22 May 2021 through 24 May 2021

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Li L, Zhou Z, Cui S. Boosting Small Ship Detection in Optical Remote Sensing Images via Image Super-Resolution. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1508-1512. (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). doi: 10.1109/CCDC52312.2021.9601674

Boosting Small Ship Detection in Optical Remote Sensing Images via Image Super-Resolution

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