Filter-based Segmentation of Automotive SAR Images

Hoffmann M, Noegel T, Schüßler C, Vossiek M, Schütz M, Gulden P (2022)

Publication Type: Conference contribution

Publication year: 2022

Event location: New York City

DOI: 10.1109/RadarConf2248738.2022.9764276

Abstract

This paper presents an approach to the segmentation of automotive synthetic aperture radar (SAR) images generated with a state-of-the-art multiple-input multiple-output frequency modulated continuous wave radar. Due to the very high image resolution and large signal-to-noise ratio, it is possible to directly distinguish different free spaces, extended objects, or target areas, such as walls or cars, and point-like targets, including posts and trees. The latter are detected using a constant false alarm rate algorithm, whereas free spaces and target areas are segmented by using Otsu's method after morphologically processing the SAR image. With this simple, filter-based segmentation, the perception of the vehicle's environment can be improved without complex post-processing that is typically required in standard radar gridmaps.

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Additional Organisation(s)

Involved external institutions

Indie Semiconductor United States (USA) (US)

How to cite

APA:

Hoffmann, M., Noegel, T., Schüßler, C., Vossiek, M., Schütz, M., & Gulden, P. (2022). Filter-based Segmentation of Automotive SAR Images. In IEEE (Eds.), Proceedings of the 2022 IEEE Radar Conference. New York City.

MLA:

Hoffmann, Marcel, et al. "Filter-based Segmentation of Automotive SAR Images." Proceedings of the 2022 IEEE Radar Conference, New York City Ed. IEEE, 2022.

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