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Tiny Object Detection in Remote Sensing Images: End-to-End Super-Resolution and Object Detection with Deep Learning
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- Author / Creator
- Rabbi, Jakaria
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In this thesis, we study the problem of detecting small objects on low-resolution (LR) satellite imagery. Small-object detection is a challenging problem, especially from LR images. To tackle the challenge, we propose a method to generate super-resolution images from low-resolution images and simultaneously detect objects from the super-resolution 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 the 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 used 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 GAN 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) dataset and another 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. While working with the detection problem, we create a GUI tool to label, train, and detect objects from remote sensing images that cover a large area. This GUI makes it easier to create small image tiles from the large satellite images, training the state-of-the-art object detection models, running the detection, and finally obtaining the output geolocation for the detected objects.
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- Graduation date
- Fall 2020
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.