Preprint Article Version 3 Preserved in Portico This version is not peer-reviewed

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Version 1 : Received: 19 March 2020 / Approved: 20 March 2020 / Online: 20 March 2020 (09:50:54 CET)
Version 2 : Received: 15 April 2020 / Approved: 16 April 2020 / Online: 16 April 2020 (03:08:50 CEST)
Version 3 : Received: 28 April 2020 / Approved: 29 April 2020 / Online: 29 April 2020 (13:33:56 CEST)

A peer-reviewed article of this Preprint also exists.

Rabbi, J.; Ray, N.; Schubert, M.; Chowdhury, S.; Chao, D. Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network. Remote Sens. 2020, 12, 1432. Rabbi, J.; Ray, N.; Schubert, M.; Chowdhury, S.; Chao, D. Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network. Remote Sens. 2020, 12, 1432.

Journal reference: Remote Sens. 2020, 12, 1432
DOI: 10.3390/rs12091432

Abstract

The detection performance of small objects in remote sensing images has not been satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) showed remarkable image enhancement performance, but reconstructed images usually miss high-frequency edge information. Therefore, object detection performance showed degradation for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we applied a new edge-enhanced super-resolution GAN (EESRGAN) to improve the quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss was backpropagated into the EESRGAN to improve the detection performance. We proposed an architecture with three components: ESRGAN, EEN, and Detection network. We used residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we used a faster region-based convolutional network (FRCNN) (two-stage detector) and a single-shot multibox detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) dataset and another self-assembled (oil and gas storage tank) satellite dataset showed superior performance of our method compared to the standalone state-of-the-art object detectors.

Supplementary and Associated Material

https://github.com/Jakaria08/EESRGAN: GitHub Repository for the implementation.

Subject Areas

object detection; faster region-based convolutional neural network (FRCNN); single-shot multibox detector (SSD); super-resolution; remote sensing imagery; edge enhancement; satellites

Comments (1)

Comment 1
Received: 29 April 2020
Commenter: JAKARIA RABBI
Commenter's Conflict of Interests: Author
Comment: 1.  Captions of the figures and tables are updated. 
2.  Corrected a lot of grammar mistakes. 
3.  The position of the Abbreviations is changed. 
4.  Added additional information in the introduction section.
5.  Headers of the tables are changed.
6.  Change in abstract 
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