Preprint Article Version 2 This version is not peer-reviewed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 is not 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) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show 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 multi-box detector (SSD); super-resolution; remote sensing imagery; edge enhancement; satellites

Comments (1)

Comment 1
Received: 16 April 2020
Commenter: JAKARIA RABBI
Commenter's Conflict of Interests: Author
Comment: -- Most of the figures are updated. -- New results are included. -- A new literature review is included. -- Corrected a lot of grammar mistakes. -- Abbreviations are included. -- New discussions are included
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