Supervised SOM Based ATR Method with Circular Polarization Basis of Full Polarimetric Data
Summary :
Satellite-borne or aircraft-borne synthetic aperture radar (SAR) is useful for high resolution imaging analysis for terrain surface monitoring or surveillance, particularly in optically harsh environments. For surveillance application, there are various approaches for automatic target recognition (ATR) of SAR images aiming at monitoring unidentified ships or aircraft. In addition, various types of analyses for full polarimetric data have been developed recently because it can provide significant information to identify structure of targets, such as vegetation, urban, sea surface areas. ATR generally consists of two processes, one is target feature extraction including target area determination, and the other is classification. In this paper, we propose novel methods for these two processes that suit full polarimetric exploitation. As the target area extraction method, we introduce a peak signal-to noise ratio (PSNR) based synthesis with full polarimetric SAR images. As the classification method, the circular polarization basis conversion is adopted to improve the robustness especially to variation of target rotation angles. Experiments on a 1/100 scale model of X-band SAR, demonstrate that our proposed method significantly improves the accuracy of target area extraction and classification, even in noisy or target rotating situations.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E98-B No.12 pp.2520-2527
- Publication Date
- 2015/12/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E98.B.2520
- Type of Manuscript
- PAPER
- Category
- Sensing
Authors
Shouhei OHNO
NEC Corpolation
Shouhei KIDERA
The University of Electro-Communications
Tetsuo KIRIMOTO
The University of Electro-Communications
Keyword
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Shouhei OHNO, Shouhei KIDERA, Tetsuo KIRIMOTO, "Supervised SOM Based ATR Method with Circular Polarization Basis of Full Polarimetric Data" in IEICE TRANSACTIONS on Communications,
vol. E98-B, no. 12, pp. 2520-2527, December 2015, doi: 10.1587/transcom.E98.B.2520.
Abstract: Satellite-borne or aircraft-borne synthetic aperture radar (SAR) is useful for high resolution imaging analysis for terrain surface monitoring or surveillance, particularly in optically harsh environments. For surveillance application, there are various approaches for automatic target recognition (ATR) of SAR images aiming at monitoring unidentified ships or aircraft. In addition, various types of analyses for full polarimetric data have been developed recently because it can provide significant information to identify structure of targets, such as vegetation, urban, sea surface areas. ATR generally consists of two processes, one is target feature extraction including target area determination, and the other is classification. In this paper, we propose novel methods for these two processes that suit full polarimetric exploitation. As the target area extraction method, we introduce a peak signal-to noise ratio (PSNR) based synthesis with full polarimetric SAR images. As the classification method, the circular polarization basis conversion is adopted to improve the robustness especially to variation of target rotation angles. Experiments on a 1/100 scale model of X-band SAR, demonstrate that our proposed method significantly improves the accuracy of target area extraction and classification, even in noisy or target rotating situations.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E98.B.2520/_p
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@ARTICLE{e98-b_12_2520,
author={Shouhei OHNO, Shouhei KIDERA, Tetsuo KIRIMOTO, },
journal={IEICE TRANSACTIONS on Communications},
title={Supervised SOM Based ATR Method with Circular Polarization Basis of Full Polarimetric Data},
year={2015},
volume={E98-B},
number={12},
pages={2520-2527},
abstract={Satellite-borne or aircraft-borne synthetic aperture radar (SAR) is useful for high resolution imaging analysis for terrain surface monitoring or surveillance, particularly in optically harsh environments. For surveillance application, there are various approaches for automatic target recognition (ATR) of SAR images aiming at monitoring unidentified ships or aircraft. In addition, various types of analyses for full polarimetric data have been developed recently because it can provide significant information to identify structure of targets, such as vegetation, urban, sea surface areas. ATR generally consists of two processes, one is target feature extraction including target area determination, and the other is classification. In this paper, we propose novel methods for these two processes that suit full polarimetric exploitation. As the target area extraction method, we introduce a peak signal-to noise ratio (PSNR) based synthesis with full polarimetric SAR images. As the classification method, the circular polarization basis conversion is adopted to improve the robustness especially to variation of target rotation angles. Experiments on a 1/100 scale model of X-band SAR, demonstrate that our proposed method significantly improves the accuracy of target area extraction and classification, even in noisy or target rotating situations.},
keywords={},
doi={10.1587/transcom.E98.B.2520},
ISSN={1745-1345},
month={December},}
Copy
TY - JOUR
TI - Supervised SOM Based ATR Method with Circular Polarization Basis of Full Polarimetric Data
T2 - IEICE TRANSACTIONS on Communications
SP - 2520
EP - 2527
AU - Shouhei OHNO
AU - Shouhei KIDERA
AU - Tetsuo KIRIMOTO
PY - 2015
DO - 10.1587/transcom.E98.B.2520
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E98-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2015
AB - Satellite-borne or aircraft-borne synthetic aperture radar (SAR) is useful for high resolution imaging analysis for terrain surface monitoring or surveillance, particularly in optically harsh environments. For surveillance application, there are various approaches for automatic target recognition (ATR) of SAR images aiming at monitoring unidentified ships or aircraft. In addition, various types of analyses for full polarimetric data have been developed recently because it can provide significant information to identify structure of targets, such as vegetation, urban, sea surface areas. ATR generally consists of two processes, one is target feature extraction including target area determination, and the other is classification. In this paper, we propose novel methods for these two processes that suit full polarimetric exploitation. As the target area extraction method, we introduce a peak signal-to noise ratio (PSNR) based synthesis with full polarimetric SAR images. As the classification method, the circular polarization basis conversion is adopted to improve the robustness especially to variation of target rotation angles. Experiments on a 1/100 scale model of X-band SAR, demonstrate that our proposed method significantly improves the accuracy of target area extraction and classification, even in noisy or target rotating situations.
ER -
English
