A Scalloping Correction Method for ScanSAR Image Based on Improved Kalman Filter Model

Yonghua CAI; Yu WANG; Huaitao FAN

doi:10.11999/JEIT200060

Volume 43 Issue 5

May 2021

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Yonghua CAI, Yu WANG, Huaitao FAN. A Scalloping Correction Method for ScanSAR Image Based on Improved Kalman Filter Model[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1212-1218. doi: 10.11999/JEIT200060

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Yonghua CAI, Yu WANG, Huaitao FAN. A Scalloping Correction Method for ScanSAR Image Based on Improved Kalman Filter Model[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1212-1218. doi: 10.11999/JEIT200060

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A Scalloping Correction Method for ScanSAR Image Based on Improved Kalman Filter Model

doi: 10.11999/JEIT200060

Yonghua CAI^{1, 2
,
,},
Yu WANG^{1, 2},
Huaitao FAN¹

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2.
School of Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Natural Science Foundation of China (61901442)

Received Date: 2020-01-15
Rev Recd Date: 2020-12-04

Available Online: 2020-12-15

Publish Date: 2021-05-18

Abstract

Abstract

The spaceborne Scanning Synthetic Aperture Radar (ScanSAR) adopts the Burst working mode. While obtaining wide-range mapping capabilities, this mode also causes an inherent scalloping in the image, which seriously affects the visual effects and quantitative applications of the image. Based on the analysis of the azimuth statistical characteristics of ScanSAR images and aimed at the shortcomings of the existing filtering model such as poor stability and high time complexity, an improved Kalman filtering model is proposed, which filters the standard deviation and mean of image in azimuth position to correct scallop stripes. The correction results on the real ScanSAR images acquired by the GF-3 satellite verify the effectiveness and efficiency of the improved algorithm. Furthermore, the experimental results on complex scene images such as buildings and the junction of sea and land indicate that the strong robustness of the improved algorithm.
- Scanning Synthetic Aperture Radar (ScanSAR),
- Scalloping,
- Radiometric correction,
- Kalman filtering,
- GF-3

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References(20)

References

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