Hyung-Sup Jung

Mapping Three-Dimensional Surface Deformation by Combining Multiple-Aperture Interferometry and Conventional Interferometry: Application to the June 2007 Eruption of Kilauea Volcano, Hawaii

Surface deformation caused by an intrusion and small eruption during June 17-19, 2007, along the East Rift Zone of Kilauea Volcano, Hawaii, was three-dimensionally reconstructed from radar interferograms acquired by the Advanced Land Observing Satellite (ALOS) phased-array type L-band synthetic aperture radar (SAR) (PALSAR) instrument. To retrieve the 3-D surface deformation, a method that combines multiple-aperture interferometry (MAI) and conventional interferometric SAR (InSAR) techniques was applied to one ascending and one descending ALOS PALSAR interferometric pair. The maximum displacements as a result of the intrusion and eruption are about 0.8, 2, and 0.7 m in the east, north, and up components, respectively. The radar-measured 3-D surface deformation agrees with GPS data from 24 sites on the volcano, and the root-mean-square errors in the east, north, and up components of the displacement are 1.6, 3.6, and 2.1 cm, respectively. Since a horizontal deformation of more than 1 m was dominantly in the north-northwest-south-southeast direction, a significant improvement of the north-south component measurement was achieved by the inclusion of MAI measurements that can reach a standard deviation of 3.6 cm. A 3-D deformation reconstruction through the combination of conventional InSAR and MAI will allow for better modeling, and hence, a more comprehensive understanding, of the source geometry associated with volcanic, seismic, and other processes that are manifested by surface deformation.

Ionospheric Correction of SAR Interferograms by Multiple-Aperture Interferometry

Interferometric synthetic aperture radar (InSAR) is a powerful technique that precisely measures surface deformations at a fine spatial resolution over a large area. However, the accuracy of this technique is sometimes compromised by ionospheric path delays on radar signals, particularly with L- and P-band SAR systems. To avoid ionospheric effects from being misinterpreted as ground displacement, it is necessary to detect and correct their contributions to interferograms. In this paper, we propose an efficient method for ionospheric measurement and correction and validate its theoretical and experimental performance. The proposed method exploits the linear relationship between the multiple-aperture interferometry phase and the azimuth derivative of the ionospheric phase. Theoretical analysis shows that a total electron content (TEC) accuracy of less than

Joint Correction of Ionosphere Noise and Orbital Error in L-Band SAR Interferometry of Interseismic Deformation in Southern California

1.0 \times 10^{-4}

A Novel Multitemporal InSAR Model for Joint Estimation of Deformation Rates and Orbital Errors

TEC units can be achieved when more than 100 neighboring samples can be averaged (multilooked), and the coherence is 0.5. The regression analysis between the interferometric phase and the topographic height shows that the root-mean-square error can be improved by a factor of two after ionospheric correction. A 2-D Fourier spectral analysis indicates that the ionospheric wave pattern in the uncorrected power spectrum has disappeared in the power spectrum of the corrected interferogram. These results demonstrate that the proposed method can effectively remove ionospheric artifacts from an ionosphere-distorted InSAR image. Note that the method assumes that there is no appreciable surface displacement in the along-track dimension of the interferogram.

Feasibility of Along-Track Displacement Measurement From Sentinel-1 Interferometric Wide-Swath Mode

The accuracy of L-band synthetic aperture radar (SAR) differential interferometry (InSAR) on crustal deformation studies is largely compromised by ionosphere path delays on the radar signals. The ionosphere effects cause severe ionospheric distortion such as azimuth streaking and long wavelength phase distortion similar to orbital ramp error. Effective detection and correction of ionospheric phase distortion from L-band InSAR images are necessary to measure and accurately interpret surface displacement. In this paper, we investigate the performance improvement of L-band InSAR interseismic deformation measurements in southern California through the joint correction of both ionosphere noise and orbital error. Our results show that this method can effectively remove orbit and ionosphere phase distortions. In comparison with in situ GPS measurements, the achieved InSAR measurement accuracy is improved from ~ 30 mm to ~ 10 mm by the proposed joint correction method. We show that, after the joint correction, the remaining atmosphere noise can be further mitigated through stacking, leading to an RMS error of ~ 4.7 mm/year in resultant line-of-sight velocity, as compared with ~ 11.3 mm/year before the correction. Our results demonstrate that the proposed joint correction technique provides a promising way to jointly correct orbital and ionospheric artifacts in L-band InSAR studies of crustal deformation.

Radar image and data fusion for natural hazards characterisation

Orbital errors, characterized typically as longwavelength artifacts, commonly exist in interferometric synthetic aperture radar (InSAR) imagery as a result of inaccurate determination of the sensor state vector. Orbital errors degrade the precision of multitemporal InSAR products (i.e., ground deformation). Although research on orbital error reduction has been ongoing for nearly two decades and several algorithms for reducing the effect of the errors are already in existence, the errors cannot always be corrected efficiently and reliably. We propose a novel model that is able to jointly estimate deformation rates and orbital errors based on the different spatial-temporal characteristics of the two types of signals. The proposed model is able to isolate a long-wavelength ground motion signal from the orbital error even when the two types of signals exhibit similar spatial patterns. The proposed algorithm is efficient and requires no ground control points. In addition, the method is built upon wrapped phases of interferograms, eliminating the need of phase unwrapping. The performance of the proposed model is validated using both simulated and real data sets. The demo codes of the proposed model are also provided for reference.

Detection and Restoration of Defective Lines in the SPOT 4 SWIR Band

The European Space Agencys Sentinel-1, a C-band imaging radar mission to be launched in mid-2013, will provide a continuity of radar data for monitoring the changing Earth. The azimuth resolution of Sentinel-1s background mode, interferometric wide-swath (IW) mode, is four times lower than that of European remote-sensing satellite (ERS) and Envisat systems. Therefore, the measurement accuracy of along-track displacement from Sentinel-1 IW images presumably will be significantly reduced. In this paper, we test the feasibility of along-track displacement measurement from Sentinel-1 IW mode. We simulate Sentinel-1 IW synthetic aperture radar (SAR) images from the ERS raw data that captured the coseismic deformation of the 1999 Hector Mine earthquake in California. Along-track displacement maps are generated using multiple-aperture interferometric SAR (MAI) and intensity tracking techniques, respectively, and are compared with GPS measurements. The root-mean-square (rms) error between the synthetic Sentinel-1 MAI and GPS measurements is about 9.6 cm, which corresponds to only 0.5 % of the azimuth resolution. The rms error between the along-track displacements from synthetic Sentinel-1 offset tracking and GPS is about 27.5 cm, which is about 1.4 % of the azimuth resolution. These results suggest that the MAI method will still be useful to measure along-track displacements from Sentinel-1 IW InSAR imagery and that it would be difficult to effectively measure the along-track displacements by the Sentinel-1 offset tracking method.

An Improvement of Ionospheric Phase Correction by Multiple-Aperture Interferometry

Fusion of synthetic aperture radar (SAR) images through interferometric, polarimetric and tomographic processing provides an all-weather imaging capability to characterise and monitor various natural hazards. This article outlines interferometric synthetic aperture radar (InSAR) processing and products and their utility for natural hazards characterisation, provides an overview of the techniques and applications related to fusion of SAR/InSAR images with optical and other images and highlights the emerging SAR fusion technologies. In addition to providing precise land-surface digital elevation maps, SAR-derived imaging products can map millimetre-scale elevation changes driven by volcanic, seismic and hydrogeologic processes, by landslides and wildfires and other natural hazards. With products derived from the fusion of SAR and other images, scientists can monitor the progress of flooding, estimate water storage changes in wetlands for improved hydrological modelling predictions and assessments of future flood impacts and map vegetation structure on a global scale and monitor its changes due to such processes as fire, volcanic eruption and deforestation. With the availability of SAR images in near real-time from multiple satellites in the near future, the fusion of SAR images with other images and data is playing an increasingly important role in understanding and forecasting natural hazards.

Theoretical Accuracy of Along-Track Displacement Measurements from Multiple-Aperture Interferometry (MAI)

This paper presents the categorization and restoration of defective lines developed in pushbroom images. About 100 of the 3000 SPOT 4 SWIR detectors malfunction, which degrades image quality. Conventional methods have difficulties in effectively detecting and restoring defective lines, because they ignore the heterogeneity of the ground surface and the presence of sporadically unstable detectors with gain and offset that vary during a scan. While all defective lines have previously been considered as a single type, here they are categorized into three types according to the variation pattern in the scanning direction: constant defective lines, irregular defective lines, and irrecoverable defective lines. The detection procedure utilizes summed data and standard deviation data that consist of abnormal peaks originating from defective lines and a slowly varying baseline reflecting the surface characteristics within the image. The defective lines are detected by finding abnormal peaks, and classified and restored by using either a moment-matching method or interpolation, depending upon their types. Three SPOT 4 images were used to test and evaluate the performance of the proposed method. From the test results, the constant defective line was the most common type, comprising about 60%, while the irregular defective lines caused serious image degradation because of the difficulty of detecting and classifying them. Commission and omission errors were less than 10% and detection accuracy was higher than 90%. The analysis of signal-to-noise ratio (SNR) showed that the low SNR created by the defective lines was effectively removed. Our method gave a significant improvement of the detection and restoration capability.

Melt Pond Mapping With High-Resolution SAR: The First View

Recently a multiple-aperture interferometry (MAI)-based azimuth shift method has been proposed to correct the ionospheric phase on synthetic aperture radar (SAR) interferograms. This method needs to determine integral constants required for azimuth integration. The exact estimation of the integral constants plays a key role in this MAI-based method. In this paper, we propose an efficient method for improving the performance of integral constant estimation, which functions by simultaneously removing the ionospheric and orbital phase artifacts from the interferometric SAR interferogram. We validate the performance improvement of the proposed method using two Advanced Land Observation Satellite Phase Array L-band SAR (ALOS PALSAR) interferometric pairs. The proposed method is compared with a MAI-based method, which does not work well due to azimuth integration errors. The proposed method successfully corrects the ionospheric and orbital phase artifacts. In addition, we compare the performances of the previous and proposed methods using the in situ Global Positioning System velocities. The root-mean-square error (RMSE) in line-of sight velocity from the previous method is about 7.0 mm/yr, whereas the RMSE from the proposed method is about 4.3 mm/yr. An RMSE reduction of about 38.6% is achieved when using the proposed method. These results indicate: 1) that the proposed method successfully estimates and corrects the ionosphere and orbital phase distortions; and 2) that the proposed method is superior to the previous method.