A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation

Koga, Yohei; Miyazaki, Hiroyuki; Shibasaki, Ryosuke

doi:10.3390/rs12030575

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Correction published on 26 March 2020, see Remote Sens. 2020, 12(7), 1068.

Open AccessArticle

A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation

Yohei Koga

^1,*,

Hiroyuki Miyazaki

and

Ryosuke Shibasaki

Independent Researcher, Tokyo 114-0000, Japan

Center for Spatial Information Science (CSIS), University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 2790000, Japan

Author to whom correspondence should be addressed.

Remote Sens. 2020, 12(3), 575; https://doi.org/10.3390/rs12030575

Received: 31 December 2019 / Revised: 28 January 2020 / Accepted: 5 February 2020 / Published: 9 February 2020

(This article belongs to the Special Issue Deep Transfer Learning for Remote Sensing)

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Abstract

Recently, object detectors based on deep learning have become widely used for vehicle detection and contributed to drastic improvement in performance measures. However, deep learning requires much training data, and detection performance notably degrades when the target area of vehicle detection (the target domain) is different from the training data (the source domain). To address this problem, we propose an unsupervised domain adaptation (DA) method that does not require labeled training data, and thus can maintain detection performance in the target domain at a low cost. We applied Correlation alignment (CORAL) DA and adversarial DA to our region-based vehicle detector and improved the detection accuracy by over 10% in the target domain. We further improved adversarial DA by utilizing the reconstruction loss to facilitate learning semantic features. Our proposed method achieved slightly better performance than the accuracy achieved with the labeled training data of the target domain. We demonstrated that our improved DA method could achieve almost the same level of accuracy at a lower cost than non-DA methods with a sufficient amount of labeled training data of the target domain. View Full-Text

Keywords: vehicle detection; domain adaptation; satellite images; single shot multibox detector (SSD); adversarial training; CORAL; reconstruction loss

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

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MDPI and ACS Style

Koga, Y.; Miyazaki, H.; Shibasaki, R. A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation. Remote Sens. 2020, 12, 575. https://doi.org/10.3390/rs12030575

AMA Style

Koga Y, Miyazaki H, Shibasaki R. A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation. Remote Sensing. 2020; 12(3):575. https://doi.org/10.3390/rs12030575

Chicago/Turabian Style

Koga, Yohei, Hiroyuki Miyazaki, and Ryosuke Shibasaki. 2020. "A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation" Remote Sensing 12, no. 3: 575. https://doi.org/10.3390/rs12030575

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A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation

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