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Band selection is a key strategy to address the challenges of managing large hyperspectral datasets and reduce the dimensionality problem associated with the simultaneous analysis of hundreds of spectral bands. However, the computational complexity of traditional methods makes the algorithms difficult to be deployed on board satellites. This is especially true for small satellites with limited computational and power resources. Moreover, the existing band selection techniques often require the hypercube to be processed at least at Level-1B product, i.e., the bands need to be finely aligned before selecting them, demanding more computational resources for the onboard computer. This study presents a novel neural-network-based approach for onboard band selection using data with coarse band-to-band aligned. This methodology not only simplifies the preprocessing requirements but also opens new possibilities for efficient hyperspectral imaging from space onboard small satellites, such as classification, change, and target detection.

Sentinel-1A/B data are crucial for retrieving numerical information about surface phenomena and processes. Coregistration of terrain observation by progressive scans (TOPS) data is a critical step in its application. TOPS data must be fundamentally co-registered with an accuracy of 0.001 pixels. However, various decorrelation factors due to natural vegetation and seasonal effects affect the coregistration accuracy of TOPS data. This paper proposed an enhanced spectral diversity coregistration method for dual-polarimetric (PolESD) Sentinel-1A/B TOPS data. The PolESD method suppresses speckle noise based on a unified non-local framework in dual-pol Synthetic Aperture Radar (SAR), and extracts the phase of the optimal polarization channel from the denoised polarimetric interferometric coherency matrix. Compared with the traditional ESD method developed for single-polarization data, the PolESD method can obtain more accurate coherence and phase and get more pixels for azimuth-offset estimation. In bare areas covered with low vegetation, the number of pixels selected by PolESD is more than the Boxcar method. It can also correct misregistration more effectively and eliminate phase jumps in the burst edge. Therefore, PolESD will help improve the application of TOPS data in low-coherence scenarios.

Full-body motion capture is essential for the study of body movement. Video-based, markerless, mocap systems are, in some cases, replacing marker-based systems, but hybrid systems are less explored. We develop methods for coregistration between 2D video and 3D marker positions when precise spatial relationships are not known a priori. We illustrate these methods on three-ball cascade juggling in which it was not possible to use marker-based tracking of the balls, and no tracking of the hands was possible due to occlusion. Using recorded video and motion capture, we aimed to transform 2D ball coordinates into 3D body space as well as recover details of hand motion. We proposed four linear coregistration methods that differ in how they optimize ball-motion constraints during hold and flight phases, using an initial estimate of hand position based on arm and wrist markers. We found that minimizing the error between ball and hand estimate was globally suboptimal, distorting ball flight trajectories. The best-performing method used gravitational constraints to transform vertical coordinates and ball-hold constraints to transform lateral coordinates. This method enabled an accurate description of ball flight as well as a reconstruction of wrist movements. We discuss these findings in the broader context of video/motion capture coregistration. [ABSTRACT FROM AUTHOR]

A significant problem in diffusion MRI (dMRI) is the lack of understanding regarding which microstructural features account for the variability in the diffusion tensor imaging (DTI) parameters observed in meningioma tumors. A common assumption is that mean diffusivity (MD) and fractional anisotropy (FA) from DTI are inversely proportional to cell density and proportional to tissue anisotropy, respectively. Although these associations have been established across a wide range of tumors, they have been challenged for interpreting within-tumor variations where several additional microstructural features have been suggested as contributing to MD and FA.To facilitate the investigation of the biological underpinnings of DTI parameters, we performed ex-vivo DTI at 200 µm isotropic resolution on sixteen excised meningioma tumor samples. The samples exhibit a variety of microstructural features because the dataset includes meningiomas of six different meningioma types and two different grades. Diffusion-weighted signal (DWI) maps, DWI maps averaged over all directions for given b-value, signal intensities without diffusion encoding (S0) as well as DTI parameters: MD, FA, in-plane FA (FAIP), axial diffusivity (AD) and radial diffusivity (RD), were coregistered to Hematoxylin & Eosin- (H&E) and Elastica van Gieson-stained (EVG) histological sections by a non-linear landmark-based approach.Here, we provide DWI signal and DTI maps coregistered to histology sections and describe the pipeline for processing the raw DTI data and the coregistration. The raw, processed, and coregistered data are hosted by Analytic Imaging Diagnostics Arena (AIDA) data hub registry, and software tools for processing are provided via GitHub. We hope that data can be used in research and education concerning the link between the meningioma microstructure and parameters obtained by DTI.

Laser speckle imaging (LSI) is a wide-field optical technique that enables superficial blood flow quantification. LSI is normally performed in a mounted configuration to decrease the likelihood of motion artifact. However, mounted LSI systems are cumbersome and difficult to transport quickly in a clinical setting for which portability is essential in providing bedside patient care. To address this issue, we created a handheld LSI device using scientific grade components. To account for motion artifact of the LSI device used in a handheld setup, we incorporated a fiducial marker (FM) into our imaging protocol and determined the difference between highest and lowest speckle contrast values for the FM within each data set (Kbest and Kworst). The difference between Kbest and Kworst in mounted and handheld setups was 8% and 52%, respectively, thereby reinforcing the need for motion artifact quantification. When using a threshold FM speckle contrast value (KFM) to identify a subset of images with an acceptable level of motion artifact, mounted and handheld LSI measurements of speckle contrast of a flow region (KFLOW) in in vitro flow phantom experiments differed by 8%. Without the use of the FM, mounted and handheld KFLOW values differed by 20%. To further validate our handheld LSI device, we compared mounted and handheld data from an in vivo porcine burn model of superficial and full thickness burns. The speckle contrast within the burn region (KBURN) of the mounted and handheld LSI data differed by

Background:  Goal: To develop a 3D visualization tool that accurately and intuitively displays critical cerebral components in epilepsy pre/post surgery.Limitations of current technology:2D images; Not patient-specific; Lock of coregistration; Non user-friendly. Faculty Adviser:  Daniel Shrey, Ph.D., Children’s Hospital Orange County; Beth Lopour, Ph.D., UC Irvine Department of Biomedical Engineering | Joffre Olaya, M.D., Pediatric Neurosurgeon

The coregistration of single-look complex (SLC) SAR images for InSAR or offset tracking applications is often performed by using an accurate DEM and precise orbital information. However, in cold regions, such DEMs are rare over high-latitude areas or not up-to-date over fast melting glaciers for instance. To overcome this difficulty, we propose in this article a coregistration method preserving InSAR phase information that only requires a 3D point of reference instead of a full DEM. Developed in a Python toolbox called LabSAR, the proposed method only uses orbital information to coregister the images on the sphere centered on the Earth center passing by the ground control point (GCP). Thanks to the use of the orbital information, the so-called orbital fringes are compensated without having to estimate them. This coregistration method is compared to other approaches in two different types of applications, InSAR and offset tracking, on a PAZ Dual-Pol Temporal Stack covering the Mont Blanc massif (western European Alps). First, InSAR measurements from LabSAR are compared with the results of the Sentinel-1 ESA toolbox (SNAP). The LabSAR interferograms exhibit clearer topographical fringes, with fewer parameters to set. Second, offset tracking based on LabSAR coregistated images is used to measure the displacement of the Bossons glacier. The results are compared with those obtained by a conventional approach developed in the EFIDIR tools. By evaluating the uncertainties of both approaches using displacements over stable areas and the temporal closure error, similar uncertainty values are found. However, velocity values differ between the two approaches, especially in areas where the altitudes are different from the altitude of the reference point. The difference can reach up to 0.06 m/day, which is in the range of the glacier velocity measurement uncertainty given in the literature. The impact of the altitude of the reference point is limited: this single GCP can be chosen at the median altitude of the study area. The error margin on the knowledge of this altitude is 1,000 m, which is sufficient for the altitude to be considered as known for a wide range of study area in the world.

Recent developments in satellite processing tools allow low-cost, fast and automatic processing of a large amount of information from Earth observation, enhancing the capability of detecting coastal changes from space at different temporal scales. Some works have assessed the quality of these data and applied it to detect coastal evolution locally, most of them focusing on mid-term and long-term changes in the coastline. In this work, we evaluate the capability to monitor changes in coastal morphology at various temporal and spatial scales using 1D (coastlines) and 3D (bathymetry) satellite-derived data obtained from site-specific processing methods. Local characteristics were included in several phases of the development of the satellite products used here: i) geolocated very high resolution images from each pilot site were used in the coregistration process to enhance geolocation accuracy in images from different missions, ii) different spectral indices were tested at each pilot site to obtain more reliable detection of the coastline at all sites and iii) measured topobathymetry data were used to obtain datum-based satellite shorelines and bathymetry. The accuracy and skill of those satellite products were assessed at several pilot sites in Spain. The results indicated high horizontal accuracy (RMSE < pixel size), with errors on the order of half of the pixel size (RMSE = 5.0 m and for Sentinel-2 and 18.8 m for Landsat5). Furthermore, the coastlines used here presented errors comparable to those obtained from the widely used open-source tool CoastSat. Time-series analysis using satellite-derived shorelines showed that coastal change processes can be detected at several temporal and spatial scales, such as short-term erosion and accretion events on a small beach, seasonal beach rotation, and long-term trends at local and regional scales. However, the results from satellite-derived bathymetry indicated that the quantitative assessment of the coastal morphology with 3D products is still limited. Some in situ measurements are necessary to obtain satellite data that represent site-specific conditions. However, the quantity of this auxiliary in situ measurements required to obtain reliable time series of satellite derived shorelines and bathymetry is significantly lower than the quantity required by traditional monitoring methods. The results are discussed, highlighting the gaps that need to be filled in the future so that satellite-derived products can be used in usual coastal change monitoring practices. • We assess the capability to monitor coastal changes with 1D and 3D satellite data. • Local characteristics are included in the development of the satellite data. • Coastal changes are detected at several temporal and spatial scales. • Long-term shoreline change in local and regional scales, and seasonal beach rotation are successfully detected. • Storm-induced erosion is monitored although cloud cover affects data availability. [ABSTRACT FROM AUTHOR]

Very high requirement of co-registration accuracy better than 0.001 pixels for Sentinel-1 TOPS (Terrain Observation by Progressive Scan) mode presents a great challenge for application of SAR (synthetic aperture radar) interferometry in Earth Observation. The state-of-the-art techniques have demonstrated that the low coherence scenarios and abrupt loss of coherence between two arbitrary acquisitions are main sources of error to degrade the performance of TOPS time-series co-registration. In this paper, we present a modification to overcome both limitations through the coherence enhancement. The motive behind this is to improve the quality of observations before co-registration and meanwhile avoiding the coherence loss caused by fast decorrelation. To this end, principal components analysis based spatio-temporal filtering is first used to remove the artifacts in burst interferograms over strong noise areas. Rather than heuristically choosing a sub-set of interferograms as a small baseline technique does, we use Dijkstra's shortest path algorithm under graph theory framework to maximize the coherence of a sub-set interferograms. The performance of presented method against the state-of-the-art techniques is fully evaluated by synthetic data and a Sentinel-1A stack over a low coherence scene in Indonesia. Comprehensive comparisons demonstrate 9%-17% uncertainty reduction of time-series co-registration when applying our method.

Abstract Background Implementation of PET/CT in diagnosis of primary prostate cancer (PCa) requires a profound knowledge about the tracer, preferably from a quantitative evaluation. Direct visual comparison of PET/CT slices to whole prostate sections is hampered by considerable uncertainties from imperfect coregistration and fundamentally different image modalities. In the current study, we present a novel method for advanced voxel-wise comparison of histopathology from excised prostates to pre-surgical PET. Resected prostates from eight patients who underwent PSMA-PET/CT were scanned (ex vivo CT) and thoroughly pathologically prepared. In vivo and ex vivo CT including histopathology were coregistered with three different methods (manual, semi−/automatic). Spatial overlap after CT-based registration was evaluated with dice similarity (DSC). Furthermore, we constructed 3D cancer distribution models from histopathologic information in various slices. Subsequent smoothing reflected the intrinsically limited spatial resolution of PSMA-PET. The resulting histoPET models were used for quantitative analysis of spatial histopathology-PET pattern agreement focusing on p values and coefficients of determination (R 2). We examined additional rigid mutual information (MI) coregistration directly based on PSMA-PET and histoPET. Results Mean DSC for the three different methods (ManReg, ScalFactReg, and DefReg) were 0.79 ± 0.06, 0.82 ± 0.04, and 0.90 ± 0.02, respectively, while quantification of PET-histopathology pattern agreement after CT-based registration revealed R 2 45.7, 43.2, and 41.3% on average with p

Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) inherently suffers from various errors. Many previous works employed Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) data to assess and enhance SRTM DEM accuracy. Nevertheless, data coregistration between the two datasets was commonly neglected in their studies. In this paper, an automated and simple three dimensional (3D) coregistration method (3CM) was introduced to align the 3-arc-second SRTM (SRTM3) DEM and ICESat/GLAS data over Jiangxi province, China. Then, accuracy evaluation of the SRTM3 DEM using ICESat/GLAS data with and without data coregistration was performed on different classes of terrain factors and different land uses, with the purpose of evaluating the importance of data coregistration. Results show that after data coregistration, the root mean square error (RMSE) and mean bias of the SRTM3 DEM are reduced by 14.4% and 97.1%, respectively. Without data coregistration, terrain aspects with a sine-like shape are strongly related to SRTM3 DEM errors; nevertheless, this relationship disappears after data coregistration. Among the six land uses, SRTM3 DEM produces the lowest accuracy in forest areas. Finally, by incorporating land uses, terrain factors and ICESat/GLAS data into the correction models, the SRTM3 DEM was enhanced using multiple linear regression (MLR), back propagation neural network (BPNN), generalized regression NN (GRNN), and random forest (RF), respectively. Results exhibit that the four enhancement models with data coregistration obviously outperform themselves without the coregistration. Among the four models, RF produces the best result, and its RMSE is about 3.1%, 2.7% and 11.3% lower than those of MLR, BPNN, and GRNN, respectively. Moreover, 146 Global Navigation Satellite System (GNSS) points over Ganzhou city of Jiangxi province were used to assess the accuracy of the RF-derived SRTM3 DEM. It is found that the DEM quality is improved and has a similar error magnitude to that relative to the ICESat/GLASS data.

Agricultural terraced landscapes, which are important historical heritage sites (e.g., UNESCO or Globally Important Agricultural Heritage Systems (GIAHS) sites) are under threat from increased soil degradation due to climate change and land abandonment. Remote sensing can assist in the assessment and monitoring of such cultural ecosystem services. However, due to the limitations imposed by rugged topography and the occurrence of vegetation, the application of a single high-resolution topography (HRT) technique is challenging in these particular agricultural environments. Therefore, data fusion of HRT techniques (terrestrial laser scanning (TLS) and aerial/terrestrial structure from motion (SfM)) was tested for the first time in this context (terraces), to the best of our knowledge, to overcome specific detection problems such as the complex topographic and landcover conditions of the terrace systems. SfM–TLS data fusion methodology was trialed in order to produce very high-resolution digital terrain models (DTMs) of two agricultural terrace areas, both characterized by the presence of vegetation that covers parts of the subvertical surfaces, complex morphology, and inaccessible areas. In the unreachable areas, it was necessary to find effective solutions to carry out HRT surveys; therefore, we tested the direct georeferencing (DG) method, exploiting onboard multifrequency GNSS receivers for unmanned aerial vehicles (UAVs) and postprocessing kinematic (PPK) data. The results showed that the fusion of data based on different methods and acquisition platforms is required to obtain accurate DTMs that reflect the real surface roughness of terrace systems without gaps in data. Moreover, in inaccessible or hazardous terrains, a combination of direct and indirect georeferencing was a useful solution to reduce the substantial inconvenience and cost of ground control point (GCP) placement. We show that in order to obtain a precise data fusion in these complex conditions, it is essential to utilize a complete and specific workflow. This workflow must incorporate all data merging issues and landcover condition problems, encompassing the survey planning step, the coregistration process, and the error analysis of the outputs. The high-resolution DTMs realized can provide a starting point for land degradation process assessment of these agriculture environments and supplies useful information to stakeholders for better management and protection of such important heritage landscapes.

Rationale and Objectives: The objective of this study was to develop and quantitatively evaluate a radiology-pathology fusion method for spatially mapping tissue regions corresponding to different chemoradiation therapy-related effects from surgically excised whole-mount rectal cancer histopathology onto preoperative magnetic resonance imaging (MRI).Materials and Methods: This study included six subjects with rectal cancer treated with chemoradiation therapy who were then imaged with a 3-T T2-weighted MRI sequence, before undergoing mesorectal excision surgery. Excised rectal specimens were sectioned, stained, and digitized as two-dimensional (2D) whole-mount slides. Annotations of residual disease, ulceration, fibrosis, muscularis propria, mucosa, fat, inflammation, and pools of mucin were made by an expert pathologist on digitized slide images. An expert radiologist and pathologist jointly established corresponding 2D sections between MRI and pathology images, as well as identified a total of 10 corresponding landmarks per case (based on visually similar structures) on both modalities (five for driving registration and five for evaluating alignment). We spatially fused the in vivo MRI and ex vivo pathology images using landmark-based registration. This allowed us to spatially map detailed annotations from 2D pathology slides onto corresponding 2D MRI sections.Results: Quantitative assessment of coregistered pathology and MRI sections revealed excellent structural alignment, with an overall deviation of 1.50 ± 0.63 mm across five expert-selected anatomic landmarks (in-plane misalignment of two to three pixels at 0.67- to 1.00-mm spatial resolution). Moreover, the T2-weighted intensity distributions were distinctly different when comparing fibrotic tissue to perirectal fat (as expected), but showed a marked overlap when comparing fibrotic tissue and residual rectal cancer.Conclusions: Our fusion methodology enabled successful and accurate localization of post-treatment effects on in vivo MRI. [ABSTRACT FROM AUTHOR]

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account--optical sequences, SAR sequences, digital elevation model--so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May-November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators. [ABSTRACT FROM AUTHOR]

The constellation of Landsat-8 and Sentinel-2 optical satellites offers opportunities for a wide range of Earth Observation (EO) applications and scientific studies in Earth sciences mainly related to geohazards. The multi-temporal co-registration accuracy of images provided by both missions is, however, currently not fully satisfactory for change detection, time-series analysis and in particular Earth surface motion measurements. The objective of this work is the development, implementation and test of an automatic processing chain for correcting co-registration artefacts targeting accurate alignment of Sentinel-2 and Landsat-8 imagery for time series analysis. The method relies on dense sub-pixel offset measurements and robust statistics to correct for systematic offsets and striping artefacts. Experimental evaluation at sites with diverse environmental settings is conducted to evaluate the efficiency of the processing chain in comparison with previously proposed routines. The experimental evaluation suggests lower residual offsets than existing methods ranging between RMSExy = 2.30 and 2.91 m remaining stable for longer time series. A first case study demonstrates the utility of the processor for the monitoring of continuously active landslides. A second case study demonstrates the use of the processor for measuring co-seismic surface displacements indicating an accuracy of 1/5 th of a pixel after corrections and 1/10th of a pixel after calibration with ground measurements. The implemented processing chain is available as an open source tool to support a better exploitation of the growing archives of Sentinel-2 and Landsat-8. [ABSTRACT FROM AUTHOR]

The performance of EEG source reconstruction has benefited from the increasing use of advanced head modeling techniques that take advantage of MRI together with the precise positions of the recording electrodes. The prevailing technique for registering EEG electrode coordinates involves electromagnetic digitization. However, the procedure adds several minutes to experiment preparation and typical digitizers may not be accurate enough for optimal source reconstruction performance (Dalal et al., 2014). Here, we present a rapid, accurate, and cost-effective alternative method to register EEG electrode positions, using a single digital SLR camera, photogrammetry software, and computer vision techniques implemented in our open-source toolbox, janus3D. Our approach uses photogrammetry to construct 3D models from multiple photographs of the participant's head wearing the EEG electrode cap. Electrodes are detected automatically or semi-automatically using a template. The rigid facial features from these photo-based models are then surface-matched to MRI-based head reconstructions to facilitate coregistration to MRI space. This method yields a final electrode coregistration error of 0.8 mm, while a standard technique using an electromagnetic digitizer yielded an error of 6.1 mm. The technique furthermore reduces preparation time, and could be extended to a multi-camera array, which would make the procedure virtually instantaneous. In addition to EEG, the technique could likewise capture the position of the fiducial markers used in magnetoencephalography systems to register head position.

Spatial normalization of brains to a standardized space is a widely used approach for group studies in functional magnetic resonance imaging (fMRI) data. Commonly used template-based approaches are complicated by signal dropout and distortions in echo planar imaging (EPI) data. The most widely used software packages implement two common template-based strategies: (1) affine transformation of the EPI data to an EPI template followed by nonlinear registration to an EPI template (EPInorm) and (2) affine transformation of the EPI data to the anatomic image for a given subject, followed by nonlinear registration of the anatomic data to an anatomic template, which produces a transformation that is applied to the EPI data (T1norm). EPI distortion correction can be used to adjust for geometric distortion of EPI relative to the T1 images. However, in practice, this EPI distortion correction step is often skipped. We compare these template-based strategies empirically in four large datasets. We find that the EPInorm approach consistently shows reduced variability across subjects, especially in the case when distortion correction is not applied. EPInorm also shows lower estimates for coregistration distances among subjects (i.e., within-dataset similarity is higher). Finally, the EPInorm approach shows higher T values in a task-based dataset. Thus, the EPInorm approach appears to amplify the power of the sample compared to the T1norm approach when not using distortion correction (i.e., the EPInorm boosts the effective sample size by 12-25%). In sum, these results argue for the use of EPInorm over the T1norm when no distortion correction is used. Hum Brain Mapp 38:5331-5342, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Background: Implementation of PET/CT in diagnosis of primary prostate cancer (PCa) requires a profound knowledge about the tracer, preferably from a quantitative evaluation. Direct visual comparison of PET/CT slices to whole prostate sections is hampered by considerable uncertainties from imperfect coregistration and fundamentally different image modalities. In the current study, we present a novel method for advanced voxel-wise comparison of histopathology from excised prostates to pre-surgical PET. Resected prostates from eight patients who underwent PSMA-PET/CT were scanned (ex vivo CT) and thoroughly pathologically prepared. In vivo and ex vivo CT including histopathology were coregistered with three different methods (manual, semi−/automatic). Spatial overlap after CT-based registration was evaluated with dice similarity (DSC). Furthermore, we constructed 3D cancer distribution models from histopathologic information in various slices. Subsequent smoothing reflected the intrinsically limited spatial resolution of PSMA-PET. The resulting histoPET models were used for quantitative analysis of spatial histopathology-PET pattern agreement focusing on p values and coefficients of determination ( R ). We examined additional rigid mutual information (MI) coregistration directly based on PSMA-PET and histoPET. Results: Mean DSC for the three different methods (ManReg, ScalFactReg, and DefReg) were 0.79 ± 0.06, 0.82 ± 0.04, and 0.90 ± 0.02, respectively, while quantification of PET-histopathology pattern agreement after CT-based registration revealed R 45.7, 43.2, and 41.3% on average with p < 10. Subsequent PET-based MI coregistration yielded R 61.3, 55.9, and 55.6%, respectively, while implying anatomically plausible transformations. Conclusions: Creating 3D histoPET models based on thorough histopathological preparation allowed sophisticated quantitative analyses showing highly significant correlations between histopathology and (PSMA-)PET. We recommend manual CT-based coregistration followed by a PET-based MI algorithm to overcome limitations of purely CT-based coregistrations for meaningful voxel-wise comparisons between PET and histopathology. [ABSTRACT FROM AUTHOR]

The performance of EEG source reconstruction has benefited from the increasing use of advanced head modeling techniques that take advantage of MRI together with the precise positions of the recording electrodes. The prevailing technique for registering EEG electrode coordinates involves electromagnetic digitization. However, the procedure adds several minutes to experiment preparation and typical digitizers may not be accurate enough for optimal source reconstruction performance (Dalal et al., 2014). Here, we present a rapid, accurate, and cost-effective alternative method to register EEG electrode positions, using a single digital SLR camera, photogrammetry software, and computer vision techniques implemented in our open-source toolbox, janus3D. Our approach uses photogrammetry to construct 3D models from multiple photographs of the participant's head wearing the EEG electrode cap. Electrodes are detected automatically or semi-automatically using a template. The rigid facial features from these photo-based models are then surface-matched to MRI-based head reconstructions to facilitate coregistration to MRI space. This method yields a final electrode coregistration error of 0.8 mm, while a standard technique using an electromagnetic digitizer yielded an error of 6.1 mm. The technique furthermore reduces preparation time, and could be extended to a multi-camera array, which would make the procedure virtually instantaneous. In addition to EEG, the technique could likewise capture the position of the fiducial markers used in magnetoencephalography systems to register head position. [ABSTRACT FROM AUTHOR]

For multitemporal analysis of synthetic aperture radar (SAR) images acquired with a terrain observation by progressive scan (TOPS) mode, all acquisitions from a given satellite track must be coregistered to a reference coordinate system with accuracies better than 0.001 of a pixel (assuming full SAR resolution) in the azimuth direction. Such a high accuracy can be achieved through geometric coregistration, using precise satellite orbits and a digital elevation model, followed by a refinement step using a time-series analysis of coregistration errors. These errors represent the misregistration between all TOPS acquisitions relative to the reference coordinate system. We develop a workflow to estimate the time series of azimuth misregistration using a network-based enhanced spectral diversity (NESD) approach, in order to reduce the impact of temporal decorrelation on coregistration. Example time series of misregistration inferred for five tracks of Sentinel-1 TOPS acquisitions indicates a maximum relative azimuth misregistration of less than 0.01 of the full azimuth resolution between the TOPS acquisitions in the studied areas. Standard deviation of the estimated misregistration time series for different stacks varies from 1.1e-3 to 2e-3 of the azimuth resolution, equivalent to 1.6–2.8 cm orbital uncertainty in the azimuth direction. These values fall within the 1-sigma orbital uncertainty of the Sentinel-1 orbits and imply that orbital uncertainty is most likely the main source of the constant azimuth misregistration between different TOPS acquisitions. We propagate the uncertainty of individual misregistration estimated with ESD to the misregistration time series estimated with NESD and investigate the different challenges for operationalizing NESD. [ABSTRACT FROM PUBLISHER]

A 41-year-old lady presented with headache and ophthalmoplegia of the left eye for 1 week and underwent magnetic resonance imaging (MRI) of the brain, which showed homogeneously enhancing nodular thickening of meninges in the left anterior and middle cranial fossa with perilesional white matter edema of the left fronto-temporal lobes which appeared suspicious for neurosarcoidosis. She subsequently underwent 18F-fluorodeoxyglucose positron emission tomography--computed tomography (18F-FDG PET/CT) to determine the disease extent and to target the optimal biopsy site. Coregistration of 18F-FDG PET--CT brain to MRI was done for better characterization of the brain lesions. [ABSTRACT FROM AUTHOR]

Hand-held based optical imaging devices are currently developed as a noninvasive method for breast cancer diagnosis. However, the devices developed to date have not performed 3D tomography since they are unable to perform coregistration. In our Optical Imaging Laboratory we have developed a hand-held optical imager with automated coregistration facilities to enable 3D tomography of breast cancer. Herein, coregistered imaging and 3D tomography are demonstrated in vitro, and preliminary in vivo studies are performed to demonstrate 2D surface mapping and coregistered imaging in breast tissue of normal human subjects. The results demonstrate potential toward clinical translation of a portable and patient-comfortable method for breast cancer diagnosis.

In Sentinel-1 TOPS mode, the antenna sweeps in the azimuth direction for the purpose of illuminating the targets with the entire azimuth antenna pattern (AAP). This azimuth sweeping introduces an extra high-frequency Doppler term into the impulse response function (IRF), which poses a more strict coregistration accuracy for the interferometric purpose. A 1/1000 pixel coregistration accuracy is required for the interferometric phase error to be negligible, and the enhanced spectral diversity (ESD) method is applied for achieving such accuracy. However, since ESD derives miscoregistration from cross-interferometric phase, and phase is always wrapped to [ − π , π ) , an initial coregistration method with enough accuracy is required to resolve the phase ambiguity in ESD. The mainstream for initial coregistration that meets this requirement is the geometrical approach, which accuracy mainly depends on the accuracy of orbits. In this article, the authors propose to investigate the feasibility of using the conventional coregistration approach, namely the cross-correlation-and-rigid-transformation, as the initial coregistration method. The aim is to quantify the coregistration accuracy for cross-correlation-and-rigid-transformation using the Cramér-Rao lower bound (CRLB) and determine whether this method could eventually help to resolve the phase ambiguities of ESD. In addition, we studied the feasibility and robustness of the cross-correlation plus ESD under different conditions. For validation, we checked whether the cross-correlation plus ESD approach could reach the same coregistration accuracy as geometrical plus ESD approach. In general, for large areas with enough coherence and little topography variance, the cross-correlation method could be used as an alternative to the geometrical approach. The interferogram from the two different approaches (with ESD applied afterward) shows a negligible difference under such circumstances.

Objectives The aim of this study was to identify histological features that correlate with terms commonly used to describe optical coherence tomographic (OCT) and optical frequency-domain imaging (OFDI) images of stented vessels, by means of a histopathological validation study using stented human coronary arteries. Background OCT imaging and OFDI are used to evaluate vascular responses to stent implantation. Descriptive terms such as “peristrut low attenuation” and “heterogeneous” have been used to describe neointimal characteristics that may have clinical relevance. However, only limited histopathological correlations are available. Methods Using the CVPath stent registry, 19 cases were identified in whom implantation duration was >30 days and OCT imaging or OFDI and histological findings were available. Consecutive OCT or OFDI frames (n = 1,063) of stented coronary arteries were categorized according to their predominant imaging features in 1-mm intervals. Coregistration of OCT or OFDI frames and histopathological cross sections was performed in 111 frames. Results Seven distinct OCT or OFDI patterns were found: homogenous (45%), layered (15%), high intensity with high attenuation (14%), intraluminal protruding masses (8%), peristrut low attenuation (7%), heterogeneous (2%), and honeycomb (1%). Histopathologically, the homogenous pattern correlated most often with smooth muscle cells within collagenous/proteoglycan matrix and less often with organized thrombus. The layered pattern correlated with healed neointimal rupture or erosion, peristrut neovascularization, or smooth muscle cells within collagen/proteoglycan matrix. High intensity with high attenuation correlated with superficial macrophage accumulation in the majority of cases, but with other histological findings in 30% of cases. The diagnostic accuracy was greater in restenotic lesions. The only OCT or OFDI finding that had a single histological feature was the honeycomb pattern. Conclusions This study suggests a lack of correlation between OCT image patterns and distinct histological tissue characteristics. [ABSTRACT FROM AUTHOR]

This paper discusses the automatic coregistration and fusion of 3d point clouds generated from aerial image sequences and corresponding thermal infrared (TIR) images. Both RGB and TIR images have been taken from a RPAS platform with a predefined flight path where every RGB image has a corresponding TIR image taken from the same position and with the same orientation with respect to the accuracy of the RPAS system and the inertial measurement unit. To remove remaining differences in the exterior orientation, different strategies for coregistering RGB and TIR images are discussed: (i) coregistration based on 2D line segments for every single TIR image and the corresponding RGB image. This method implies a mainly planar scene to avoid mismatches; (ii) coregistration of both the dense 3D point clouds from RGB images and from TIR images by coregistering 2D image projections of both point clouds; (iii) coregistration based on 2D line segments in every single TIR image and 3D line segments extracted from intersections of planes fitted in the segmented dense 3D point cloud; (iv) coregistration of both the dense 3D point clouds from RGB images and from TIR images using both ICP and an adapted version based on corresponding segmented planes; (v) coregistration of both image sets based on point features. The quality is measured by comparing the differences of the back projection of homologous points in both corrected RGB and TIR images. [ABSTRACT FROM AUTHOR]

Processing scanning synthetic aperture radar (ScanSAR) data using a stripmap processor, which is called full-aperture processing, has been the choice of many researchers. ScanSAR data are known to require very high azimuth coregistration precision which is usually achieved by a geometrical coregistration followed by a spectral diversity coregistration on the ScanSAR burst. However, for full-aperture processing, since individual bursts are no longer available for spectral diversity coregistration, the cross-correlation method in practice is still used to coregister ScanSAR data as stripmap data. We analyze the azimuth coregistration precision requirement of full-aperture processing and find that its requirement can be significantly relaxed. This is confirmed by a number of experiments, including simulations and real data experiments whose results are in good agreement with each other. An additional experiment on the cross-correlation method supports its use in full-aperture processing. Concluding from the experimental results, we further propose a simple method to evaluate the azimuth coregistration precision requirement for practical use. Finally, we present examples with ALOS-2 ScanSAR data. [ABSTRACT FROM AUTHOR]

Synergistic applications based on integrated hyperspectral and lidar data are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar data is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of information and does not exploit the full potential of both sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne data, based on their respective adopted return intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In additional to a tie-point-based coregistration, we introduce a ray-tracing-based back projection of the lidar intensities for area-based cost aggregation. The approach consists of three processing steps. First is a coarse automatic tie-point-based boresight alignment. The second step coregisters the hyperspectral data to the lidar intensities. Third is a parametric coalignment refinement with an area-based cost aggregation. This hybrid approach of combining tie-point features and area-based cost aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities results in a root-mean-square error of 1/3 pixel. It indicates that a highly integrated and stringent combination of different coalignment methods leads to an improvement of the multisensor coregistration. [ABSTRACT FROM AUTHOR]

Sentinel-1 (S-1) has an unparalleled mapping capacity. In interferometric wide swath (IW) mode, three subswaths imaged in the novel Terrain Observation by Progressive Scans (TOPS) SAR mode result in a total swath width of 250 km. S-1 has become the European workhorse for large area mapping and interferometric monitoring at medium resolution. The interferometric processing of TOPS data however requires special consideration of the signal properties, resulting from the ScanSAR-type burst imaging and the antenna beam steering in azimuth. The high Doppler rate in azimuth sets very stringent coregistration requirements, making the use of enhanced spectral diversity (ESD) necessary to obtain the required fine azimuth coregistration accuracy. Other unique aspects of processing IW data, such as azimuth spectral filtering, image resampling, and data deramping and reramping, are reviewed, giving a recipe-like description that enables the user community to use S-1 IW mode repeat-pass SAR data. Interferometric results from S-1A are provided, demonstrating the mapping capacity of the S-1 system and its interferometric suitability for geophysical applications. An interferometric evaluation of a coherent interferometric pair over Salar de Uyuni, Bolivia, is provided, where several aspects related to coregistration, deramping, and synchronization are analyzed. Additionally, a spatiotemporal evaluation of the along-track shifts, which are directly related to the orbital/instrument timing error, measured from the SAR data is shown, which justifies the necessity to refine the azimuth shifts with ESD. The spatial evaluation indicates high stability of the azimuth shifts for several slices of a datatake. [ABSTRACT FROM PUBLISHER]

Background: Diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) abnormalities in patients with multiple sclerosis (MS) are currently measured by a complex combination of separate procedures. Therefore, the purpose of this study was to provide a reliable method for reducing analysis complexity and obtaining reproducible results. Methods: We implemented a semi-automated measuring system in which different well-known software components for magnetic resonance imaging (MRI) analysis are integrated to obtain reliable measurements of DWI and PWI disturbances in MS. Results: We generated the Diffusion/Perfusion Project (DPP) Suite, in which a series of external software programs are managed and harmonically and hierarchically incorporated by in-house developed Matlab software to perform the following processes: 1) image pre-processing, including imaging data anonymization and conversion from DICOM to Nifti format; 2) co-registration of 2D and 3D non-enhanced and Gd-enhanced T1-weighted images in fluid-attenuated inversion recovery (FLAIR) space; 3) lesion segmentation and classification, in which FLAIR lesions are at first segmented and then categorized according to their presumed evolution; 4) co-registration of segmented FLAIR lesion in T1 space to obtain the FLAIR lesion mask in the T1 space; 5) normal appearing tissue segmentation, in which T1 lesion mask is used to segment basal ganglia/thalami, normal appearing grey matter (NAGM) and normal appearing white matter (NAWM); 6) DWI and PWI map generation; 7) co-registration of basal ganglia/thalami, NAGM, NAWM, DWI and PWI maps in previously segmented FLAIR space; 8) data analysis. All these steps are automatic, except for lesion segmentation and classification. Conclusion: We developed a promising method to limit misclassifications and user errors, providing clinical researchers with a practical and reproducible tool to measure DWI and PWI changes in MS. [ABSTRACT FROM AUTHOR]

Terrain observation by progressive scan (TOPS) antenna beam steering is utilized for European Space Agency’s (ESA’s) Sentinel-1 synthetic aperture radar (SAR) sensor for the interferometric wide swath (IW) and extra wide swath (EW) modes. As a consequence of the azimuth steering, the resulting signal characteristics have to be accounted for in SAR interferometric (InSAR) processing. This paper assesses the performance of speckle tracking and spectral diversity (SD) [also referred to as split spectrum or multi-aperture interferometry (MAI)] when applied to TOPS data acquired over nonstationary scenarios, such as glaciers. The characteristics of the TOPS signal, especially the azimuth-variant Doppler centroid, are discussed with particular consideration of along-track surface motion between the interferometric acquisitions. The TOPS specific coregistration requirements are formulated, followed by an analysis of the theoretical estimation accuracy as a function of the estimation window size. A refined adaptive coregistration approach based on SD is suggested. Experimental TerraSAR-X TOPS data acquired over the Lambert glacier, Antarctica, are used to validate the proposed speckle tracking and SD methodologies. [ABSTRACT FROM PUBLISHER]

Eyetracking during reading has provided a critical source of on-line behavioral data informing basic theory in language processing. Similarly, event-related potentials have provided an important on-line measure of the neural correlates of language processing. Recently there has been strong interest in co-registering eyetracking and ERPs from simultaneous recording to capitalize on the strengths of both techniques, but a challenge has been devising approaches for controlling artifacts produced by eye movements in the EEG waveform. In this paper we describe our approach to correcting for eye movements in EEG and demonstrate its applicability to reading. The method is based on independent components analysis, and uses three criteria for identifying components tied to saccades: (1) component loadings on the surface of the head are consistent with eye movements; (2) source analysis localizes component activity to the eyes, and (3) the temporal activation of the component occurred at the time of the eye movement and differed for right and left eye movements. We demonstrate this method’s applicability to reading by comparing ERPs time-locked to fixation onset in two reading conditions. In the text-reading condition, participants read paragraphs of text. In the pseudo-reading control condition, participants moved their eyes through spatially similar pseudo-text that preserved word locations, word shapes, and paragraph spatial structure, but eliminated meaning. The corrected EEG, time-locked to fixation onsets, showed effects of reading condition in early ERP components. The results indicate that co-registration of eyetracking and EEG in connected-text paragraph reading is possible, and has the potential to become an important tool for investigating the cognitive and neural bases of on-line language processing in reading.

An advantage of fluorescence methods over other imaging modalities is the ability to concurrently resolve multiple moieties using fluorochromes emitting at different spectral regions. Simultaneous imaging of spectrally separated agents is helpful in interrogating multiple functions or establishing internal controls for accurate measurements. Herein, we investigated multimoiety imaging in the context of a limited-projection-angle hybrid fluorescence molecular tomography (FMT), and x-ray computed tomography implementation and the further registration with positron emission tomography (PET) data. Multichannel FMT systems may image fluorescent probes of varying distribution patterns. Therefore, it is possible that different channels may require different use of priors and regularization parameters. We examined the performance of automatically estimating regularization factors implementing priors, using data-driven regularization specific for limited-projectionangle schemes. We were particularly interested in identifying the implementation variations between hybridFMT channels due to probe distribution variation. For this reason, initial validation of the data-driven algorithm on a phantom was followed by imaging different agent distributions in animals, assuming superficial and deep seated activity. We further demonstrate the benefits of combining hybrid FMT with PET to gain multiple readings on the molecular composition of disease. [ABSTRACT FROM AUTHOR]

A large archive of digital elevation model (DEM) data spanning more than a decade is now available for a wide range of earth surface process studies. Precise coregistration is a critical step for obtaining accurate elevation change measurements from repeat DEMs. We present an algorithm for coregistering stereoscopic gridded DEMs that includes an automatic detection of control surfaces, a reliable estimation technique for defining a geometric relationship between DEMs through a 3-D conformal transformation, and a quality check (QC) for the confidence of the coregistration. For the control surface detection, we use a geometric constraint approach that uses similarities in slope, aspect, and height undulation as observation weights in an iterative transformation model solution. The QC is possible through the evaluation of the geometric similarities and the amount of detected relatively unchanged surfaces. We test our algorithm using a sample of repeat stereoscopic gridded DEMs from multiple sources over a rapidly changing glacier in Greenland. The algorithm results, on average, in a 24% decrease in the standard error, as obtained from elevation over stationary surfaces. The result is not sensitive to the distribution of the control surface. The QC metric provides a simple and effective criterion for parsing result quality in large data sets. Surface height decreases of ~ 110 and 200 m at the up-glacier and down-glacier ends of Kangerdlugssuaq Glacier in Greenland are observed between April 2001 and June 2010, respectively. [ABSTRACT FROM PUBLISHER]

Eyetracking during reading has provided a critical source of on-line behavioral data informing basic theory in language processing. Similarly, event-related potentials (ERPs) have provided an important on-line measure of the neural correlates of language processing. Recently there has been strong interest in co-registering eyetracking and ERPs from simultaneous recording to capitalize on the strengths of both techniques, but a challenge has been devising approaches for controlling artifacts produced by eye movements in the EEG waveform. In this paper we describe our approach to correcting for eye movements in EEG and demonstrate its applicability to reading. The method is based on independent components analysis, and uses three criteria for identifying components tied to saccades: (1) component loadings on the surface of the head are consistent with eye movements; (2) source analysis localizes component activity to the eyes, and (3) the temporal activation of the component occurred at the time of the eye movement and differed for right and left eye movements. We demonstrate this method's applicability to reading by comparing ERPs time-locked to fixation onset in two reading conditions. In the text-reading condition, participants read paragraphs of text. In the pseudo-reading control condition, participants moved their eyes through spatially similar pseudo-text that preserved word locations, word shapes, and paragraph spatial structure, but eliminated meaning. The corrected EEG, time-locked to fixation onsets, showed effects of reading condition in early ERP components. The results indicate that co-registration of eyetracking and EEG in connected-text paragraph reading is possible, and has the potential to become an important tool for investigating the cognitive and neural bases of on-line language processing in reading. [ABSTRACT FROM AUTHOR]

Cropland mapping in smallholder landscapes is challenged by complex and fragmented landscapes, labor-intensive and unmechanized land management causing high within-field variability, rapid dynamics in shifting cultivation systems, and substantial proportions of short-term fallows. To overcome these challenges, we here present a large-area mapping framework to identify active cropland and short-term fallows in smallholder landscapes for the 2020/2021 growing season at 4.77 m spatial resolution. Our study focuses on Northern Mozambique, an area comprising 381,698 km 2 . The approach is based on Google Earth Engine and time series of PlanetScope mosaics made openly available through Norwaýs International Climate and Forest Initiative (NICFI) data program. We conducted multi-temporal coregistration of the PlanetScope data using seasonal Sentinel-2 base images and derived consistent and gap-free seasonal time series metrics to classify active cropland and short-term fallows. An iterative active learning framework based on Random Forest class probabilities was used for training rare classes and uncertain regions. The map was accurate (area-adjusted overall accuracy 88.6% ± 1.5%), with the main error type being the commission of active cropland. Error-adjusted area estimates of active cropland extent (61,799.5 km 2  ± 4,252.5 km 2 ) revealed that existing global and regional land cover products tend to under-, or over-estimate active cropland extent, respectively. Short-term fallows occupied 28.9% of the cropland in our reference sample (13% of the mapped cropland), with consolidated agricultural regions showing the highest shares of short-term fallows. Our approach relies on openly available PlanetScope data and cloud-based processing in Google Earth Engine, which minimizes financial constraints and maximizes replicability of the methods. All code and maps were made available for further use., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Brain source localization accuracy in magnetoencephalography (MEG) requires accuracy in both digitizing anatomical landmarks and coregistering to anatomical magnetic resonance images (MRI). We compared the source localization accuracy and MEG-MRI coregistration accuracy of two head digitization systems—a laser scanner and the current standard electromagnetic digitization system (Polhemus)—using a calibrated phantom and human data. When compared using the calibrated phantom, surface and source localization accuracy for data acquired with the laser scanner improved over the Polhemus by 141% and 132%, respectively. Laser scan digitization reduced MEG source localization error by 1.38 mm on average. In human participants, a laser scan of the face generated a 1000-fold more points per unit time than the Polhemus head digitization. An automated surface-matching algorithm improved the accuracy of MEG-MRI coregistration over the equivalent manual procedure. Simulations showed that the laser scan coverage could be reduced to an area around the eyes only while maintaining coregistration accuracy, suggesting that acquisition time can be substantially reduced. Our results show that the laser scanner can both reduce setup time and improve localization accuracy, in comparison to the Polhemus digitization system. [ABSTRACT FROM PUBLISHER]

Abstract: To obtain an accurate 3D model of the real world using a laser scanner, point clouds should be registered precisely. To increase the registration accuracy, the authors used wavelet based noise removal filters on the point cloud data, and extracted feature points from intensity images using the SIFT (scale-invariant feature transformation) method for two overlapping point clouds. These feature points were then used for a corresponding point matching to obtain a rigid body transformation matrix by an iterative technique. With initial CTNC (closest-to-next-closest) ratio of 0.4, points were extracted and the transformation matrix was calculated. Under this geometric condition, the CTNC ratio was increased to obtain more points for matching. Then, the transformation matrix was recalculated with these points, giving more reliable results. The outliers were removed by random sample consensus (RANSAC) processing. To measure and analyze the performance of our approach in pairwise registration, additional transformation parameters were computed using the Polyworks commercial software. Comparison of the two methods showed no significant difference in mm level. In the final stage, all the scan data are rapidly adjusted using global registration, due to a small number of accurate control points. Thus, the proposed coregistration method can be used to obtain fast 3D modeling results on construction sites where registration targets cannot be installed. [Copyright &y& Elsevier]

We describe an approach for improving the robustness to misregistration of pixel-wise anomalous change detection (ACD) algorithms. The aim of ACD is to distinguish actual anomalous changes from the irrelevant incidental differences that occur throughout the scene. For such change detection to be effective, it is important that corresponding pixels in the two images of interest correspond to the same location in the scene. Indeed, one of the most confounding sources of incidental differences is the inevitable imprecision in the coregistration of the two images. We address this with small local adjustments to the coregistration which leads to a modified misregistration-insensitive measure of anomalousness. Several variants are considered, and the resulting performance improvements are evaluated using both real and simulated changes, and real and simulated misregistration. [ABSTRACT FROM PUBLISHER]

This paper presents results on SAR interferometry for data acquired in the Terrain Observation by Progressive Scans (TOPS) imaging mode. The rationale to retrieve accurate interferometric products in this mode is expounded, emphasizing the critical step of coregistration. Due to the particularities of the TOPS mode, a high Doppler centroid is present at burst edges, demanding a very high azimuth coregistration performance. A coregistration accuracy of around one tenth of a pixel, as it is usually recommended for stripmap interferometric data, could result in large undesired azimuth phase ramps in each TOPS burst. This paper presents two approaches based on the spectral diversity technique to precisely estimate this coregistration offset with the required accuracy and evaluates their performance. The effect of squint at burst edges in terms of an undesired impulse response shift during focusing and the impact on the interferometric coregistration performance is also addressed. Repeat-pass TOPS data acquired experimentally by TerraSAR-X are used to validate the proposed approaches. [ABSTRACT FROM PUBLISHER]