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Article

An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition

School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
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Remote Sens. 2020, 12(16), 2582; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162582
Received: 4 July 2020 / Revised: 29 July 2020 / Accepted: 5 August 2020 / Published: 11 August 2020
(This article belongs to the Special Issue Data Fusion for Urban Applications)
The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as a third-order tensor. The InSAR tensor can be filtered by data fusion, i.e., tensor decomposition, and these filters keep balance in the noise elimination and the fringe details preservation, especially with abrupt fringe change, e.g., the edge of urban structures. However, tensor decomposition based on batch processing cannot deal with few newly acquired interferograms filtering directly. The filtering of dynamic InSAR tensor is the inevitable challenge when processing InSAR stack data, where dynamic InSAR tensor denotes the size of InSAR tensor increases continuously due to the acquisition of new interferograms. Therefore, based on the online CANDECAMP/PARAFAC (CP) decomposition, we propose an online filter to fuse data and process the dynamic InSAR tensor, named OLCP-InSAR, which performs well especially for the urban area. In this method, CP rank is utilized to measure the tensor sparsity, which can maintain the structural features of the InSAR tensor. Additionally, CP rank estimation is applied as an important step to improve the robustness of Online CP decomposition - InSAR(OLCP-InSAR). Importing CP rank and outlier’s position as prior information, the filter fuses the noisy interferograms and decomposes the InSAR tensor to acquire the low rank information, i.e., filtered result. Moreover, this method can not only operate on tensor model, but also efficiently filter the new acquired interferogram as matrix model with the assistance of chosen low rank information. Compared with other tensor-based filters, e.g., high order robust principal component analysis (HoRPCA) and Kronecker-basis-representation multi-pass SAR interferometry (KBR-InSAR), and the widespread traditional filters operating on a single interferometric pair, e.g., Goldstein, non-local synthetic aperture radar (NL-SAR), non-local InSAR (NL-InSAR), and InSAR nonlocal block-matching 3-D (InSAR-BM3D), the effectiveness and robustness of OLCP-InSAR are proved in simulated and real InSAR stack data. Especially, OLCP-InSAR can maintain the fringe details at the regular building top with high noise intensity and high outlier ratio. View Full-Text
Keywords: multi-pass SAR interferometry (InSAR); data fusion; online tensor decomposition; phase filtering multi-pass SAR interferometry (InSAR); data fusion; online tensor decomposition; phase filtering
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MDPI and ACS Style

You, Y.; Wang, R.; Zhou, W. An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition. Remote Sens. 2020, 12, 2582. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162582

AMA Style

You Y, Wang R, Zhou W. An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition. Remote Sensing. 2020; 12(16):2582. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162582

Chicago/Turabian Style

You, Yanan, Rui Wang, and Wenli Zhou. 2020. "An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition" Remote Sensing 12, no. 16: 2582. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162582

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