Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points – A Review

Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings

Seafloor depth estimation by means of interferometric synthetic aperture sonar

The topic of this thesis is relative depth estimation using interferometric sidelooking sonar. We give a thorough description of the geometry of interferometric sonar and of time delay estimation techniques. We present a novel solution for the depth estimate using sidelooking sonar, and review the cross-correlation function, the cross-uncertainty function and the phase-differencing technique. We find an elegant solution to co-registration and unwrapping by interpolating the sonar data in ground-range. Two depth estimation techniques are developed: Cross-correlation based sidescan bathymetry and synthetic aperture sonar (SAS) interferometry. We define flank length as a measure of the horizontal resolution in bathymetric maps and find that both sidescan bathymetry and SAS interferometry achieve theoretical resolutions. The vertical precision of our two methods are close to the performance predicted from the measured coherence. We study absolute phase-difference estimation using bandwidth and find a very simple split-bandwidth approach which outperforms a standard 2D phase unwrapper on complicated objects. We also examine advanced filtering of depth maps. Finally, we present pipeline surveying as an example application of interferometric SAS

Satellite investigations of ice-ocean interactions in the Amundsen Sea sector of West Antarctica

This thesis analyses satellite-based radar data to improve our understanding of the interactions between the Antarctic Ice Sheet and the ocean in the Amundsen Sea Sector of West Antarctica. Over the last two decades, the European Remote Sensing (ERS) Satellites have provided extensive observations of the marine and cryospheric environments of this region. Here I use this data record to develop new datasets and methods for studying the nature and drivers of ongoing change in this sector. Firstly, I develop a new bathymetric map of the Amundsen Sea, which serves to provide improved boundary conditions for models of (1) ocean heat transfer to the ice sheet margin, and (2) past ice sheet behaviour and extent. This new map augments sparse ship-based depth soundings with dense gravity data acquired from ERS altimetry and achieves an RMS depth accuracy of 120 meters. An evaluation of this technique indicates that the inclusion of gravity data improves the depth accuracy by up to 17 % and reveals glaciologically-important features in regions devoid of ship surveys. Secondly, I use ERS synthetic aperture radar observations of the tidal motion of ice shelves to assess the accuracy of tide models in the Amundsen Sea. Tide models contribute to simulations of ocean circulation and are used to remove unwanted signals from estimates of ice shelf flow velocities. The quality of tide models directly affects the accuracy of such estimates yet, due to a lack of in situ records, tide model accuracy in this region is poorly constrained. Here I use two methods to determine that tide model accuracy in the Amundsen Sea is of the order of 10 cm. Finally, I develop a method to map 2-d ice shelf flow velocity from stacked conventional and multiple aperture radar interferograms. Estimates of ice shelf flow provide detail of catchment stability, and the processes driving glaciological change in the Amundsen Sea. However, velocity estimates can be contaminated by ocean tide and atmospheric pressure signals. I minimise these signals by stacking interferograms, a process which synthesises a longer observation period, and enhances long-period (flow) displacement signals, relative to rapidly-varying (tide and atmospheric pressure) ones. This avoids the reliance upon model predictions of tide and atmospheric pressure, which can be uncertain in remote regions. Ice loss from Amundsen Sea glaciers forms the largest component of Antarctica’s total contribution to sea level, yet because present models cannot adequately characterise the processes driving this system, future glacier evolution is uncertain. Observations and models implicate the ocean as the driver of glaciological change in this region and have focussed attention on improving our understanding of the nature of ice-ocean interactions in the Amundsen Sea. This thesis contributes datasets and methods that will aid historical reconstructions, current monitoring and future modelling of these processes

Matching of repeat remote sensing images for precise analysis of mass movements

Photogrammetry, together with radar interferometry, is the most popular of the remote sensing techniques used to monitor stability of high mountain slopes. By using two images of an area taken from different view angles, photogrammetry produces digital terrain models (DTM) and orthoprojected images. Repeat digital terrain models (DTM) are differenced to compute elevation changes. Repeat orthoimages are matched to compute the horizontal displacement and deformation of the masses. The success of the photogrammetric approach in the computation of horizontal displacement (and also the generation of DTM through parallax matching, although not covered in this work) greatly relies on the success of image matching techniques. The area-based image matching technique with the normalized cross-correlation (NCC) as its similarity measure is widely used in mass movement analysis. This method has some limitations that reduce its precision and reliability compared to its theoretical potential. The precision with which the matching position is located is limited to the pixel size unless some sub-pixel precision procedures are applied. The NCC is only reliable in cases where there is no significant deformation except shift in position. Identification of a matching entity that contains optimum signal-to-noise ratio (SNR) and minimum geometric distortion at each location has always been challenging. Deformation parameters such as strains can only be computed from the inter-template displacement gradient in a post-matching process. To find appropriate solutions for the mentioned limitations, the following investigations were made on three different types of mass movements; namely, glacier flow, rockglacier creep and land sliding. The effects of ground pixel size on the accuracy of the computed mass movement parameters such as displacement were investigated. Different sub-pixel precision algorithms were implemented and evaluated to identify the most precise and reliable algorithm. In one approach images are interpolated to higher spatial resolution prior to matching. In another approach the NCC correlation surface is interpolated to higher resolution so that the location of the correlation peak is more precise. In yet another approach the position of the NCC peak is computed by fitting 2D Gaussian and parabolic curves to the correlation peak turn by turn. The results show that the mean error in metric unit increases linearly with the ground pixel size being about half a pixel at each resolution. The proportion of undetected moving masse increases with ground pixel size depending on the displacement magnitudes. Proportion of mismatching templates increases with increasing ground pixel size depending on the noise content, i.e. temporal difference, of the image pairs. Of the sub-pixel precision algorithms, interpolating the image to higher resolution using bi-cubic convolution prior to matching performs best. For example, by increasing the spatial resolution (i.e. reducing the ground pixel size) of the matched images by 2 to 16 times using intensity interpolation, 40% to 80% of the performances of the same resolution original image can be achieved. A new spatially adaptive algorithm that defines the template sizes by optimizing the SNR, minimizing the geometric distortion and optimizing the similarity measure was also devised, implemented and evaluated on aerial and satellite images of mass movements. The algorithm can also exclude ambiguous and occluded entities from the matching. The evaluation of the algorithm was conducted on simulated deformation images and in relation to the image-wide fixed template sizes ranging from 11 to 101 pixels. The evaluation of the algorithm on the real mass movements is conducted by a novel technique of reconstructing the reference image from the deformed image and computing the global correlation coefficient and the corresponding SNR between the reference and the reconstructed image. The results show that the algorithm could reduce the error of displacement estimation by up to over 90% (in the simulated case) and improve the SNR of the matching by up to over 4 times compared to the globally fixed template sizes. The algorithm pushes terrain displacement measurement from repeat images one step forward towards full automation. The least squares image matching (LSM) matches images precisely by modeling both the geometric and radiometric deformation. The potential of the LSM is not fully utilized for mass movement analysis. Here, the procedures with which horizontal surface displacement, rotation and strain rates of glacier flow, rockglacier creep and land sliding are computed from the spatial transformation parameters of LSM automatically during the matching are implemented and evaluated. The results show that the approach computes longitudinal strain rates, transverse strain rates and shear strain rates reliably with mean absolute deviation in the order of 10-4 as evaluated on stable grounds. The LSM also improves the accuracy of displacement estimation of the NCC by about 90% in ideal (simulated) case and the SNR of the matching by about 25% in real multi-temporal images of mass movements. Additionally, advanced spatial transformation models such as projective and second degree polynomial are used for the first time for mass movement analysis in addition to the affine. They are also adapted spatially based on the minimization of the sum of square deviation between the matching templates. The spatially adaptive approach produces the best matching, closely followed by the second-order polynomial. Affine and projective models show similar results closely following the two approaches. In the case of the spatially adaptive approach, over 60% of the entities matched for the rockglacier and the landslide are best fit by the second-order polynomial model. In general, the NCC alone may be sufficient for low resolution images of moving masses with limited or no deformation. To gain better precision and reliability in such cases, the template sizes can be adapted spatially and the images can be interpolated to higher resolution (preferably not more detail than 1/16th of a pixel) prior to the matching. For highly deformed masses where higher resolution images are used, the LSM is recommended as it results in more accurate matching and deformation parameters. Improved accuracy and precision are obtained by selecting matchable areas using the spatially adaptive algorithm, identifying approximate matches using the NCC and optimizing the matches and measuring the deformation parameters using the LSM algorithm

Interferometric Synthetic Aperture RADAR and Radargrammetry towards the Categorization of Building Changes

The purpose of this work is the investigation of SAR techniques relying on multi image acquisition for fully automatic and rapid change detection analysis at building level. In particular, the benefits and limitations of a complementary use of two specific SAR techniques, InSAR and radargrammetry, in an emergency context are examined in term of quickness, globality and accuracy. The analysis is performed using spaceborne SAR data

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports