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Proceedings Paper

Geospatial object detection using deep networks
Author(s): Onur Barut; A. Aydin Alatan
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Paper Abstract

In the last decade, deep learning has been drawing a huge interest due to the developments in the computational hardware and novel machine learning techniques. This progress also significantly effects satellite image analysis for various objectives, such as disaster and crisis management, forest cover, road mapping, city planning and even military purposes. For all these applications, detection of geospatial objects has crucial importance and some recent object detection techniques are still unexplored to be applied for satellite imagery. In this study, aircraft, building, and ship detection in 4-band remote sensing images by using convolutional neural networks based on popular YOLO network is examined and the accuracy comparison between 4-band and 3-band images are tested. Based on simulation results, it can be concluded that state-of-the-art object detectors can be utilized for geospatial objection detection purposes.

Paper Details

Date Published: 9 September 2019
PDF: 9 pages
Proc. SPIE 11127, Earth Observing Systems XXIV, 1112708 (9 September 2019); doi: 10.1117/12.2530027
Show Author Affiliations
Onur Barut, Middle East Technical Univ. (Turkey)
Univ. of Massachusetts, Lowell (United States)
A. Aydin Alatan, Middle East Technical Univ. (Turkey)

Published in SPIE Proceedings Vol. 11127:
Earth Observing Systems XXIV
James J. Butler; Xiaoxiong (Jack) Xiong; Xingfa Gu, Editor(s)

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