Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack
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Programa de Pós-Graduação em Estatística, Universidade Federal de Pernambuco, Recife 50670-901, Brazil
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Departamento de Engenharia de Telecomunicações, Universidade Federal do Pampa, Alegrete 97546-550, Brazil
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Departamento de Engenharia Elétrica, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil
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Department of Mathematics and Natural Sciences, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden
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Department of Telecommunications, Aeronautics Institute of Technology (ITA), São José dos Campos, SP 12228-900, Brazil
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Signal Processing Group and Departamento de Estatística, Universidade Federal Pernambuco, Recife 50670-901, Brazil
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Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N1N4, Canada
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Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33199, USA
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Departamento de Estatística and LACESM, Universidade Federal de Santa Maria, Santa Maria 97105-900, Brazil
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Saab Electronic Defence Systems, 412 76 Gothenburg, Sweden
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2008; https://doi.org/10.3390/s20072008
Received: 13 February 2020 / Revised: 23 March 2020 / Accepted: 31 March 2020 / Published: 3 April 2020
(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)
This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of and a false alarm rate of , when considering military vehicles concealed in a forest.
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Keywords:
CARABAS II; ground scene prediction; image stack; multi-pass; SAR images
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Palm, B.G.; Alves, D.I.; Pettersson, M.I.; Vu, V.T.; Machado, R.; Cintra, R.J.; Bayer, F.M.; Dammert, P.; Hellsten, H. Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack. Sensors 2020, 20, 2008. https://doi.org/10.3390/s20072008
AMA Style
Palm BG, Alves DI, Pettersson MI, Vu VT, Machado R, Cintra RJ, Bayer FM, Dammert P, Hellsten H. Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack. Sensors. 2020; 20(7):2008. https://doi.org/10.3390/s20072008
Chicago/Turabian StylePalm, Bruna G.; Alves, Dimas I.; Pettersson, Mats I.; Vu, Viet T.; Machado, Renato; Cintra, Renato J.; Bayer, Fábio M.; Dammert, Patrik; Hellsten, Hans. 2020. "Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack" Sensors 20, no. 7: 2008. https://doi.org/10.3390/s20072008
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