Geospatial Object Detection Using Deep Networks

Date

2019-01-01

Author

Barut, Onur
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Convolutional neural network, Remote sensing, Object detection, Multiband satellite images

URI

https://hdl.handle.net/11511/45826

DOI

https://doi.org/10.1117/12.2530027

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

O. Barut and A. A. Alatan, “Geospatial Object Detection Using Deep Networks,” 2019, vol. 11127, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/45826.