Framework of Jitter Detection and Compensation for High Resolution Satellites

Tong, Xiaohua; Ye, Zhen; Xu, Yangsheng; Tang, Xinming; Liu, Shijie; Li, Lingyun; Xie, Huan; Wang, Fengxiang; Li, Tianpeng; Hong, Zhonghua

doi:10.3390/rs6053944

Cited by 55 publications

(32 citation statements)

References 29 publications

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“…These unexpected strips correspond to unmodeled satellite attitude jitter during the image acquisition, which can be further analyzed and compensated using raw data without geometric processing [25], [26].…”

Section: ) Results Of the Dense Matchingmentioning

confidence: 99%

“…In addition, phase correlation has also drawn more and more attention in the remote sensing community, in applications such as pixelto-pixel coregistration [12], narrow baseline digital elevation model (DEM) generation [13], [14], in-flight calibration [15], [16], and surface dynamics measurement, including coseismic deformation measurement [17]- [19], glacier displacement survey [1], [20], and sand dune migration investigation [21], [22]. This paper concentrates on introducing subpixel image registration for translation estimation, which is fundamental to many applications in the fields of remote sensing and image processing, such as earth surface dynamics monitoring [23], [24], attitude jitter analysis [25], [26], image super-resolution [27], and optical flow [28].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Subpixel Phase Correlation Method Using Singular Value Decomposition and Unified Random Sample Consensus

Tong

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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Subpixel translation estimation using phase correlation is a fundamental task for numerous applications in the remote sensing community. The major drawback of the existing subpixel phase correlation methods lies in their sensitivity to corruption, including aliasing and noise, as well as the poor performance in the case of practical remote sensing data. This paper presents a novel subpixel phase correlation method using singular value decomposition (SVD) and the unified random sample consensus (RANSAC) algorithm. In the proposed method, SVD theoretically converts the translation estimation problem to one dimensions for simplicity and efficiency, and the unified RANSAC algorithm acts as a robust estimator for the line fitting, in this case for the high accuracy, stability, and robustness. The proposed method integrates the advantages of Hoge's method and the RANSAC algorithm and avoids the corresponding shortfalls of the original phase correlation method based only on SVD. A pixel-to-pixel dense matching scheme on the basis of the proposed method is also developed for practical image registration. Experiments with both simulated and real data were carried out to test the proposed method. In the simulated case, the comparative results estimated from the generated synthetic image pairs indicate that the proposed method outperforms the other existing methods in the presence of both aliasing and noise, in both accuracy and robustness. Moreover, the pixel locking effect that commonly occurs in subpixel matching was also investigated. The degree of pixel locking effect was found to be significantly weakened by the proposed method, as compared with the original Hoge's method. In the real data case, experiments using different bands of ZY-3 multispectral sensor-corrected images demonstrate the promising performance and feasibility of the proposed method, which is able to identify seams of the image stitching between sub-charge-coupled device units.Index Terms-Aliasing and noise, singular value decomposition (SVD), subpixel phase correlation, unified random sample consensus.

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Section: ) Results Of the Dense Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Subpixel Phase Correlation Method Using Singular Value Decomposition and Unified Random Sample Consensus

Tong

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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“…A low-pass Gaussian function was additionally used on the normalized cross-power spectrum for spectral weighting. (10) Upsampling. This algorithm directly resampled the PC similarity values to a higher resolution based on the matrix-multiplication implementation of discrete Fourier transform [20], as shown in Equation (9).…”

Section: Algorithmic Implementationsmentioning

confidence: 99%

“…From the acquired displacement fields, the property and status of the observed scene and target can be retrieved, such as the height information within the scene [6], the ground surface deformation caused by geologic processes and climate change [7], and the motion of targets [8]. In addition, the quality of the sensor and geometric processing can be assessed according to the displacement fields, reflecting the sensor distortions [9], the attitude variations [10], the inconsistencies after correction or stitching [11], and other information. Therefore, dense image matching is an indispensable study area and has been broadly used in a variety of applications in the remote sensing community [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Taking normalized cross-correlation (NCC) in the spatial domain or phase correlation (PC) in the frequency domain as a similarity measure, twelve matching algorithms were tested: Centroid-NCC [41], SimiFit-NCC [7], SimiFit-SGM (semi-global matching) [42], LSM [26], IC-GN (inverse compositional Gauss-Newton) [43], MicMac [27], Correlation flow [44], Centroid-OC (orientation correlation) [41], SimiFit-PC [45], Upsampling [20], SVD-RANSAC (singular value decomposition-random sample consensus) [46], COSI-Corr-Freq [7]. These algorithms were evaluated in a simulated experiment with varying amounts of aliasing and two real applications of attitude jitter detection [10] and disparity estimation [47]. This study was not a complete study that intended to investigate all subpixel algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

Xu²,

Chen

et al. 2020

Remote Sensing

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Dense image matching is a crucial step in many image processing tasks. Subpixel accuracy and fractional measurement are commonly pursued, considering the image resolution and application requirement, especially in the field of remote sensing. In this study, we conducted a practical analysis and comparative study on area-based dense image matching with subpixel accuracy for remote sensing applications, with a specific focus on the subpixel capability and robustness. Twelve representative matching algorithms with two types of correlation-based similarity measures and seven types of subpixel methods were selected. The existing matching algorithms were compared and evaluated in a simulated experiment using synthetic image pairs with varying amounts of aliasing and two real applications of attitude jitter detection and disparity estimation. The experimental results indicate that there are two types of systematic errors: displacement-dependent errors, depending on the fractional values of displacement, and displacement-independent errors represented as unexpected wave artifacts in this study. In addition, the strengths and limitations of different matching algorithms on the robustness to these two types of systematic errors were investigated and discussed.

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