A new approach to the histogram-based segmentation of synthetic aperture radar images

• Autorzy: Siemiatkowska Barbara , Gromada Krzysztof

Identyfikatory

DOI

10.14313/JAMRIS/1‐2021/5

• Języki publikacji: EN

Abstrakty

EN

Radar machine vision is an emerging research field in the mobile robotics. Because Synthetic Aperture Radars (SAR) are robust against weather and light condition, they pro‐ vide more useful and reliable information than optical images. On the other hand, the data processing is more complicated and less researched than visible light images processing. The main goal of our research is to build sim‐ ple and efficient method of SAR image analysis. In this ar‐ ticle we describe our research related to SAR image seg‐ mentation and attempts to detect elements such as the buildings, roads and forest areas. Tests were carried out for the images made available by Leonardo Airborne & Space System Company.

Słowa kluczowe

EN

SAR image; machine vision

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2021
• Tom: Vol. 15, No. 1
• Strony: 39--42
• Opis fizyczny: Bibliogr. 14 poz., rys.

Twórcy

autor

Siemiatkowska Barbara

• Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland

autor

Gromada Krzysztof

• PIT‑RADWAR, Warsaw, Poland

Bibliografia

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• [12] H. Yu, X. Zhang, S. Wang, and B. Hou, “Context‑Based Hierarchical Unequal Merging for SAR Image Segmentation”, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 2, 2013, 995–1009, 10.1109/TGRS.2012.2203604.
• [13] Y. Zhong, A. Ma, and L. Zhang, “An Adaptive Memetic Fuzzy Clustering Algorithm With Spatial Information for Remote Sensing Imagery”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 4, 2014, 1235–1248, 10.1109/JSTARS.2014.2303634.
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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).