An Image Stabilization Method for Airborne Video SAR Based on a Joint Singer-Random Walk Model.
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| Title: | An Image Stabilization Method for Airborne Video SAR Based on a Joint Singer-Random Walk Model. |
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| Authors: | Wang, Yanping1 (AUTHOR), Wang, Shuo1,2 (AUTHOR), Wang, Zhirui3,4 (AUTHOR), Wang, Guanyong1,4 (AUTHOR) guanbingwang@126.com |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1500. 24p. |
| Subjects: | Image stabilization, Synthetic aperture radar, Kalman filtering, Motion estimation (Signal processing), Image registration |
| Abstract: | Highlights: What are the main findings? Conventional NCC-based registration directly uses frame-by-frame drift estimates for image stabilization, but the estimates still contain systematic bias and random errors, resulting in residual interframe jitter. This work redefines NCC-based drift estimates as drift measurements and establishes a joint state space model for long-duration airborne ViSAR image stabilization, where the true drift is represented by a two-dimensional Singer process and the systematic registration bias by a random walk process. Real airborne W-band ViSAR experiments show that the proposed method suppresses both interframe drift and residual jitter more effectively than direct NCC-based registration, reducing the two-dimensional drift RMSE from 3.10 to 1.31 pixels (with an error reduction rate of approximately 58%) while achieving the highest difference image entropy in each frame. What are the implications of the main findings? The proposed method provides an effective solution for high-precision stabilization of long-duration airborne ViSAR image sequences, improving scene consistency and stability in multiframe imaging. By explicitly separating true drift from slowly varying systematic bias in NCC-based measurements, this work offers a practical state space estimation framework for enhancing the reliability of subsequent ViSAR applications such as scene surveillance, moving target discrimination, and target tracking. Video synthetic aperture radar (ViSAR) provides continuous multiframe images while maintaining high resolution and has become an important tool for complex scene surveillance and moving target tracking. ViSAR imaging is susceptible to interframe drift caused by motion errors, which severely degrades video stability. When registering long time series of real airborne video SAR images, conventional image registration based on Normalized Cross-Correlation (NCC) is affected by several factors, including platform residual motion errors, approximations in the imaging geometry, interpolation resampling, and SAR speckle noise. As a result, noticeable interframe jitter persists in the registered sequence, and the stabilization accuracy is insufficient to meet high-precision image stabilization requirements. To address these issues, this paper proposes an image stabilization method for airborne video SAR based on a joint Singer-random walk model. Firstly, with the first frame selected as the reference, subpixel drift measurements in the azimuth and range directions are extracted from continuous frames via NCC-based registration. Subsequently, the true drift is modeled as a two-dimensional Singer process and the systematic bias as a random walk process, yielding a joint state space model that comprises displacement, velocity, acceleration, and bias components. On this basis, a Kalman filter and a Rauch–Tung–Striebel (RTS) fixed-interval smoother are applied to perform temporal filtering and trajectory smoothing on the drift measurements, thereby producing smooth two-dimensional drift estimates that closely approximate the actual drift trajectory. Finally, the smoothed drift trajectory is used to perform frame-by-frame subpixel drift correction on the original image sequence, achieving high-precision interframe stabilization of the ViSAR imagery. The results of real data processing demonstrate that the proposed method can effectively improve the consistency and scene stability of ViSAR multi-frame imaging. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Conventional NCC-based registration directly uses frame-by-frame drift estimates for image stabilization, but the estimates still contain systematic bias and random errors, resulting in residual interframe jitter. This work redefines NCC-based drift estimates as drift measurements and establishes a joint state space model for long-duration airborne ViSAR image stabilization, where the true drift is represented by a two-dimensional Singer process and the systematic registration bias by a random walk process. Real airborne W-band ViSAR experiments show that the proposed method suppresses both interframe drift and residual jitter more effectively than direct NCC-based registration, reducing the two-dimensional drift RMSE from 3.10 to 1.31 pixels (with an error reduction rate of approximately 58%) while achieving the highest difference image entropy in each frame. What are the implications of the main findings? The proposed method provides an effective solution for high-precision stabilization of long-duration airborne ViSAR image sequences, improving scene consistency and stability in multiframe imaging. By explicitly separating true drift from slowly varying systematic bias in NCC-based measurements, this work offers a practical state space estimation framework for enhancing the reliability of subsequent ViSAR applications such as scene surveillance, moving target discrimination, and target tracking. Video synthetic aperture radar (ViSAR) provides continuous multiframe images while maintaining high resolution and has become an important tool for complex scene surveillance and moving target tracking. ViSAR imaging is susceptible to interframe drift caused by motion errors, which severely degrades video stability. When registering long time series of real airborne video SAR images, conventional image registration based on Normalized Cross-Correlation (NCC) is affected by several factors, including platform residual motion errors, approximations in the imaging geometry, interpolation resampling, and SAR speckle noise. As a result, noticeable interframe jitter persists in the registered sequence, and the stabilization accuracy is insufficient to meet high-precision image stabilization requirements. To address these issues, this paper proposes an image stabilization method for airborne video SAR based on a joint Singer-random walk model. Firstly, with the first frame selected as the reference, subpixel drift measurements in the azimuth and range directions are extracted from continuous frames via NCC-based registration. Subsequently, the true drift is modeled as a two-dimensional Singer process and the systematic bias as a random walk process, yielding a joint state space model that comprises displacement, velocity, acceleration, and bias components. On this basis, a Kalman filter and a Rauch–Tung–Striebel (RTS) fixed-interval smoother are applied to perform temporal filtering and trajectory smoothing on the drift measurements, thereby producing smooth two-dimensional drift estimates that closely approximate the actual drift trajectory. Finally, the smoothed drift trajectory is used to perform frame-by-frame subpixel drift correction on the original image sequence, achieving high-precision interframe stabilization of the ViSAR imagery. The results of real data processing demonstrate that the proposed method can effectively improve the consistency and scene stability of ViSAR multi-frame imaging. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101500 |
