Residue Classification-Based Robust Phase Unwrapping in High-Noise Region

Chen, Xiaomao; Wen, Zhongyuan; Wu, Qi

doi:10.1155/2022/5423881

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…The Bayesian algorithms include the extended Kalman filtering phase unwrapping algorithm [7], the unscented Kalman filtering phase unwrapping algorithm, the iterated unscented Kalman filtering phase unwrapping method, an unscented Kalman filtering method with fading factor inserted [8,9], and so on. Integrated denoising and unwrapping methods overcome the problems of nonlinear phase differences and noise, but converting nonlinear systems to linear systems can result in the loss of high-order phase information, reducing the accuracy [10,11] of phase unwrapping. In addition, due to the high computational time consumption, they are not suitable for real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet

Chen,

Zhang,

Qin

et al. 2024

Remote Sensing

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Two-dimensional phase unwrapping (2-D PU) is vital for reconstructing Earth’s surface topography and displacement from interferometric synthetic aperture radar (InSAR) data. Conventional algorithms rely on the postulate, but this assumption is often insufficient due to abrupt topographic changes and severe noise. To address this challenge, our research proposes a novel approach utilizing deep convolutional neural networks inspired by the U-Net architecture to estimate phase gradient information. Our approach involves downsampling the input data to extract crucial features, followed by upsampling to restore spatial resolution. We incorporate two attention mechanisms—feature pyramid attention (FPA) and global attention upsample (GAU)—and a residual structure in the network’s structure. Thus, we construct ResDANet (residual and dual attention net). We rigorously train ResDANet utilizing simulated datasets and employ an L1-norm objective function to minimize the disparity between unwrapped phase gradients and those calculated by ResDANet, yielding the final 2-D PU results. The network is rigorously trained using two distinct training strategies and encompassing three types of simulated datasets. ResDANet exhibits excellent robust performance and efficiency on simulated data and real data, such as China’s Three Gorges and an Italian volcano.

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Section: Introductionmentioning

confidence: 99%

Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet

Chen,

Zhang,

Qin

et al. 2024

Remote Sensing

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“…The branch-cut PhU [74] is a technique used to abandon phase discontinuities or jumps in a signal, by selecting a branch cut in the complex plane and adding an integer multiple of 2𝜋 across the cut to make the phase function continuous. To reduce the effect of noise transmission, the method iteratively estimates the unwrapped phase values starting from a reference point but averting the integration path from crossing the branch cuts [81]. The objective function of the shortest path for the 𝑚 × 𝑛 interferogram can be formulated as [81]:…”

Section: Branch-cut Methodsmentioning

confidence: 99%

“…To reduce the effect of noise transmission, the method iteratively estimates the unwrapped phase values starting from a reference point but averting the integration path from crossing the branch cuts [81]. The objective function of the shortest path for the 𝑚 × 𝑛 interferogram can be formulated as [81]:…”

Section: Branch-cut Methodsmentioning

confidence: 99%

Spatio-temporal quality metrics for satellite SAR interferometric data

Wassie

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(English) The availability of RADAR images for monitoring Earth’s surface and its changes over time prompt the development of several interferometric SAR (InSAR) algorithms and methodologies. The Multi-temporal InSAR (MT-InSAR) techniques- being the one, generally, aim at extracting the temporal evolution of displacements of targets with coherent scattering behavior from a stack of SAR acquisitions that are taken from the same area and processed relative to temporal and spatial references. On the other hand, due to the complex nature of SAR measurements, addressing all possible model parameters in a comprehensive manner is still challenging and is a source of error and misinterpretation. As a well-established spaceborne geodetic technique with various applications, the accuracy of MT-InSAR products does matter, and hence potential error sources must be mitigated. The accuracy of displacement time-series (TS) estimates, in many cases, is governed by the quality and number of exploited images, associated interferograms, and selected measurement points. Such data and derived results are heavily influenced by factors, including environmental conditions at times of measurement acquisition and the processing algorithms used. Incomplete representations of factors in modeling MT-InSAR measurements may, as well, lead to omission errors and hence an incorrect estimation and interpretation of the displacement TS. Any uncertainty in the accuracy of the MT-InSAR products compromises their reliability in sensitive applications. In line with this, tools to detect, identify, and classify interferometric measurements that are particularly impacted by phase unwrapping (PhU) errors had been developed with the aim to identify and mitigate potential errors in the data— ultimately improving the quality and valuing the worth of MT-InSAR measurements. These tools make use of estimated phase residuals derived from a redundant network of unwrapped interferograms to formulate threshold-based quality scores as indicators of the reliability of the measurement points, images in a TS, images, and interferograms. Multiple pairs of baseline-constrained Sentinel-1 SAR images were considered to assess the potential of the tools to automatically detect, identify, and mitigate errors in the framework of the PSIG processing chain. The techniques were applied pixel by pixel in space and time to associate reliability scores highlighting the influence of global and local PhU errors in the course of the phase estimation. Experiments had generally justified that error mitigation strategies which include the exclusion of less reliable measurements from the network had relieved the impacts of errors and improved the TS estimation. Another important aspect of the thesis is the use of the point score tool as a post-PhU pixel filtering technique. The methodology contributes by providing quantitative information on the percentage of reliable pixels immediately after the PhU step. The experiments in this regard justified that the tool adds extra dimensions to common pixel selection techniques and is responsible for mitigating limitations on PhU error correction methodologies. Using it in conjunction with existing point selection techniques further guaranteed the inclusion of reliable points in the process. The results demonstrated that the proposed techniques significantly improved the quality of InSAR data. Besides, implications for various applications that rely on MT-InSAR datasets— such as those used in monitoring mining areas and infrastructures and those ground deformations induced either by natural or anthropogenic phenomena, have been justified. Potential avenues for future research to further improve the qualities of interferometric products including the quantification of interferometric measurement point location uncertainties are also envisioned. (Español) La creciente disponibilidad de imágenes RADAR para monitorear la superficie de la Tierra y sus cambios a lo largo del tiempo ha impulsado el desarrollo de varios algoritmos y metodologías de SAR (Synthetic Aperture Radar) interferométrico (InSAR). Las conocidas técnicas Multi-temporales InSAR (MT-InSAR) tienen como objetivo extraer la evolución temporal de los desplazamientos de puntos medidos. Esto se realiza sobre blancos con un comportamiento coherente en una pila de adquisiciones SAR sobre la misma área. Debido a la naturaleza compleja de las mediciones SAR, abordar todos los parámetros posibles del modelo de manera integral sigue siendo un desafío y es una fuente de error. Como una técnica geodésica bien establecida, la precisión de los productos MT-InSAR es importante y, por lo tanto, se deben mitigar las posibles fuentes de error. Cualquier incertidumbre en la precisión de los productos MT-InSAR compromete su fiabilidad en aplicaciones sensibles. En línea con esto, en esta tesis se han desarrollado herramientas para detectar, identificar y clasificar las mediciones interferométricas que se ven particularmente afectadas por los errores de desarrollo de fase, con el objetivo de identificar y mitigar posibles errores en los datos, mejorando entonces la calidad de las mediciones de MT-InSAR. Estas herramientas utilizan residuos de fase estimados derivados de una red redundante de interferogramas enrollados para formular índices de calidad basados en umbrales como indicadores de la fiabilidad de los puntos de medición, de las imágenes en una serie temporal, en imágenes y en interferogramas. Las técnicas se aplicaron píxel por píxel en el espacio y en el tiempo para asociar índices de fiabilidad que resaltaran la influencia de los errores de desarrollado de fase globales y locales. Los experimentos corroboran la estrategia propuesta. Otro aspecto importante de la tesis es el uso de la herramienta de índice de fiabilidad como técnica de selección de píxeles. La metodología proporciona información cuantitativa sobre el porcentaje de píxeles fiables inmediatamente después del desarrollo de fase. En general, los resultados demostraron que las técnicas propuestas mejoraron significativamente la calidad de los datos InSAR, por ejemplo, al excluir puntos erróneos automáticamente a partir de millones de puntos de medición. Además, se han justificado las implicaciones para varias aplicaciones que se basan en conjuntos de datos MT-InSAR, como los que se utilizan para monitorizar áreas e infraestructuras mineras y aquellas deformaciones delsuelo inducidas por fenómenos naturales o antropogénicos. Se prevén posibles vías de investigación futura para mejorar aún más las cualidades de los productos interferométricos.

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An Efficient Phase Unwrapping Method Based on Unscented Kalman Filter

Chen

Huang

et al. 2023

IEEE J. Miniat. Air Space Syst.

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Residue Classification-Based Robust Phase Unwrapping in High-Noise Region

Cited by 3 publications

References 29 publications

Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet

Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet

Spatio-temporal quality metrics for satellite SAR interferometric data

An Efficient Phase Unwrapping Method Based on Unscented Kalman Filter

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