Many studies on synthetic aperture radar automatic target recognition (SAR ATR) have aimed to enhance recognition accuracy. Using across-track interferometry (XTI) provides height information of targets, and the 3-D structure of targets in 2-D SAR images can be effectively employed to improve target recognition accuracy. Therefore, this study aims to enhance target detection and recognition accuracy by simultaneously using both conventional amplitude images and XTI interferograms from simulation data in training. The research compared target detection and recognition accuracy between training with only single-band amplitude images generated by RaySAR and training with dual-band images combining amplitude images and XTI interferograms. The results showed that dual-band images significantly outperformed single-band images, with an overall 15.53% increase in$F_{1}$score. This advancement enhances the practical applicability of SAR ATR in various conditions and for various targets, especially with the increasing availability of satellites capable of operating XTI.

This study addresses the limitations in target variety and real-world applicability in synthetic aperture radar automatic target recognition (SAR ATR) by enhancing recognition accuracy using both amplitude images and cross-track interferometry (XTI) from simulation data. XTI provides critical height and 3D structural information of targets in 2D SAR images, which is pivotal for improving recognition accuracy. The research focused on comparing the effectiveness of training models with conventional single-band amplitude images versus double-band images that combine amplitude images with XTI interferograms. The study utilized RaySAR to generate these images. Results demonstrated that the double-band approach significantly outperforms the single-band in precision and recall, yielding a notable 22.20% increase in the F1score. This significant improvement in recognition accuracy enhances the practical utility of SAR ATR in diverse conditions and targets, aligning well with the growing availability of satellites capable of XTI operations.

The W-band Synthetic Aperture Radar (SAR) system can be significantly utilized in autonomous vehicle systems. Accurate estimation of the reference signal phase is essential for reconstructing high-resolution and well-focused SAR images. This necessitates precise distance information between the radar and objects, implying the need for three-dimensional spatial information of the surrounding environment. In this study, we developed an automotive W-band SAR system using a 77 GHz frequency modulation continuous wave radar and a stereo camera. The three-dimensional spatial information of the surroundings was captured using the stereo camera. Optimized six degrees of freedom were achieved using gradient descent based on entropy, allowing for precise alignment of the radar coordinates with the point cloud. Our automotive W-band SAR system successfully reconstructed high-resolution, clear SAR images (vertical cross-sections) of street trees. Compared to traditional SAR systems, our system demonstrated an improvement in structural similarity index by approximately 0.15.

This study presented an effective algorithm that measures target azimuth and range velocity of vessels in SAR image without assistance of AIS information. As the SAR Doppler history was modified from target velocity, azimuth and range velocity caused the target to be defocused and shifted, respectively. The proposed algorithm utilized phase refocusing function and sub-aperture analysis which calibrated the distortions and accordingly derived two-dimensional velocity of the target in a precise manner.

In this letter, we present a new semi-empirical approach for optimized dark spot detection. The proposed method is a C-band model that identifies oil spill candidates by correlating surface wind vectors and incidence angles with SAR threshold backscatter coefficients. Specifically, our approach combines insights from wind-wave generation and radar imaging theories with the 193 global Sentinel-1 SAR oil spill data. Demonstrating a balance between accuracy and simplicity, which is crucial in dark spot detection, it achieves an F1 score of 0.8 and operates without requiring parametric adjustments or iterative processes. Its integration of wind speed data could also reduce false alarms in low-wind scenarios. These results indicate potential improvements in the efficiency of automated oil spill detection systems and their application in creating a balanced deep learning dataset.

Along with vessel detection, vessel recognition in high-resolution SAR images was necessary in order to monitor marine vessels effectively. However, lack of target data and phase defocusing of target from its velocity limited the recognition performance, especially when using detectors based on artificial intelligence. This study accordingly proposed effective vessel recognition in high-resolution ICEYE spotlight SAR images consecutively utilizing (i) vessel detector robust to defocused moving vessels and (ii) mitigation of moving target phase distortion. In order to apply quantitative and qualitative training data enhancement, a target velocity SAR phase refocusing function was developed. The proposed target velocity SAR phase refocusing function generated defocused SLC image with respect to different target azimuth velocity, which can be utilized for both training data augmentation and refocusing of velocity-induced phase distortion. Achievement of stable vessel recognition performance was enabled from (i) robust vessel detection on defocused moving vessels and (ii) well-focused detected vessel targets, both of which were consecutively applied using the proposed target velocity SAR phase refocusing function. Vessel detection results demonstrated robust performance regardless of vessel motion and vessel recognition results significantly improved after phase refocusing, both of which were subject to quantitative and qualitative training data enhancement. Performance of the proposed algorithm was analyzed both in terms of phase focusing and velocity estimation. Refocusing performance outperformed that of conventional state-of-the-art autofocusing algorithm, modified Phase Gradient Autofocusing, while azimuth velocity estimation derived the average offset of 0.68 m/s, which was regarded more accurate than previous azimuth velocity estimators based on single-channel SAR image.

This study quantitatively measured vessel rotational motion from satellite SAR data using a target motion phase refocusing function. In contrast to horizontal linear motion of vessel, vessel rotational motion caused by ocean wave force was not able to be monitored from AIS information and accordingly rarely studied. Nevertheless, extensive vessel rotational motion was directly related to vessel motion hazard and potential of maritime accident, which was required to be measured in order to thoroughly analyze vessel movement. A target motion SAR phase refocusing function was proposed, which effectively measured vessel velocity, acceleration, and accordingly its rotational motion, conventionally named as yawing, pitching, and rolling motion. A total of 29 vessels whose velocity exceeding 1 m/s were selected from three different satellite SAR images and analyzed using the target motion SAR phase refocusing function. It precisely measured vessel velocity and acceleration, and subsequently derived horizontal and vertical vessel angular accelerations, which reorganized vessel yawing, pitching, and rolling motion. When compared with AIS-driven motion, estimated azimuth velocity and horizontally projected range acceleration respectively derived RMSE offset of 0.49 m/s and 0.0032 m/s2. Moreover, the proposed phase refocusing function outperformed the conventional SAR phase focusing algorithms in aspect of focusing performance. As the measurement of the vessel rotational motion using the proposed refocusing function was presented, it can be practically applied to monitoring vessel motion hazard in ocean using satellite SAR data.

Military target identification is critical for defense and intelligence operations, but acquiring labeled Synthetic Aperture Radar (SAR) data poses challenges. This paper introduces an innovative approach that leverages SAR simulation to generate labeled data, addressing security constraints, operational limitations, and high costs associated with data collection. The integration of both phase and amplitude images obtained from SAR simulation was investigated to enhance target identification accuracy and robustness. SAR simulation offers a cost-effective and scalable solution, accurately modeling the radar imaging process to create large quantities of labeled data resembling real-world scenarios. The Cross-Track Interferometry SAR (XTI-SAR), utilizing a vertical baseline in the direction of platform flight, was employed to convert phase information into target height. The outcomes contribute to advancing SAR-based target identification techniques, providing valuable insights for effective and efficient target identification in complex operational scenarios.

This study focuses on improving the geometric accuracy of small satellite Synthetic Aperture Radar (SAR) images through software techniques. Small satellites are increasingly being launched in the new space era, using cost-effective components like Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU). However, the resulting satellite images may have relatively poor quality. Geometric accuracy is crucial for satellite images, influenced by sensor acquisition geometry. While ground control points (GCPs) are commonly used for corrections, accurate calibration for ocean SAR images is limited. This study proposes using Automatic Identification System (AIS) data to interpolate ship positions and employing deep learning technology for ship detection, repeatedly matching AIS data with SAR images to enhance geometric accuracy. The effectiveness of this technique is evaluated using HiSEA-1 SAR images and demonstrated improvement is observed with an increasing number of ships.

High-resolution SAR images have become essential data in various fields such as object detection and disaster analysis. However, obtaining high-resolution images is relatively difficult and expensive, leading to ongoing research on methods to generate them. Recently, deep learning has been explored to convert low-resolution images into high-resolution ones. In this paper, we utilize deep learning to generate high-resolution images, and we modify and combine loss functions to enhance the model’s performance. The employed loss function incorporates various functions such as SSIM, MS-SSIM, L1, and L2. The experimental results demonstrate that utilizing the combined loss function outperforms using the existing loss function alone for image generation. When comparing the indicators, the combined loss function yields an SSIM value of 0.6098 and a DISTS value of 0.0542, indicating a 5% improvement over the original loss function.

SAR images have been widely used in the detection of oil spills due to their differing backscattering coefficients allowing them to be easily distinguished from the oil-free surface of the sea. However, the insufficient training data and the presence of look-alikes have always been challenges in oil spill detection. A novel SAR-based self-evolving oil spill detection algorithm was proposed which can automatically generate new training data from whole SAR images and improve its performance through model retraining with training dataset updates. The algorithm was tested on a total of eight SAR images with satisfactory results. We expect that our algorithm will be an effective tool for oil spill detection. Significantly, the self-evolving nature of our algorithm allows it to improve over time with more SAR data.

The environmental threat posed by marine oil pollution emphasizes the importance of precise oil spill detection in SAR imagery. This research proposes a new threshold model for oil spill detection, incorporating significant parameters that influence ocean sea state and SAR imaging. The model framework, which accounts for key parameters, is calibrated by incorporating oil spill observational data obtained from the Sentinel-1 satellite. Compared to other threshold methods such as the Otsu and Bradley adaptive models, the proposed model exhibits a superior F1 score of 0.7923. It is important to note that the model effectively eliminates low-wind areas, which is a predominant false positives in threshold models.

As Doppler rate in azimuth compression of SAR image was estimated from immutable targets, moving objects in image coverage inevitably confronted azimuth defocusing. A phase compensation filter from target azimuth velocity was implemented to ICEYE SAR SLC images, optimized by minimizing Entropy. Conventional object detection algorithm based on artificial intelligence demonstrated the amelioration of detection performance in the refocused SAR images.

Rising relative sea level changes due to the ground subsidence in the coast can cause the risk of flooding in the low-lying areas. In this study, we estimated the subsidence along the coast for 50km length using time-series SAR Interferometry technique by acquiring Sentinel-1 SAR data of 78 scenes in descending and 81 scenes in ascending mode. Also, subsidence analysis at critical infrastructures such as Busan new port and Gimhae airport were conducted for analyzing the risk of flooding due to rapid subsidence.

Mitigation of geometric calibration offset in ocean without utilizing ground control points was investigated in this study. Real-time AIS information on vessels was exploited after preprocessing and accordingly tested against the detected vessels in the SAR image. Repetitive procedure of measuring the offset between the AIS sensor and the vessel detection was conducted and derived the SAR image of which the positioning offset was ameliorated. The proposed geo-location enhancement algorithm demonstrated the possibility of application in real-time vessel monitoring from remote sensing.

Flooding is one of the most serious disasters in Asian counties. Satellites can detect such floods well over large areas, and SAR satellites are particularly useful in determining the extent of flood damage at night or in cloudy weather. However, in order for the SAR satellite to be of practical help in reducing flood damage, the information must be provided to decision makers quickly, and such a real-time monitoring system for Asian countries has not yet been operated. In this study, a fully automated warning system that can quickly detect and display flood events occurring in East Asia was developed using AWS's S3, Lambda, and EC2.

Real-time surveillance of vessel in ocean was previously enforced by AIS sensors, while the importance of weather independent monitoring apparatus, SAR, is currently being implemented. For practical application of SAR image based vessel monitoring however, correction of azimuth shift caused by the velocity of moving target was necessary. This study presented azimuth shift compensation on vessels detected from Cosmo-SkyMed SAR images using machine learning based object detector. Conventional azimuth velocity measurement using Doppler frequency estimation was implemented and transformed into azimuth shift. AIS information corresponding to SAR images was preprocessed in order to be employed as a reference. From three Cosmo-SkyMed SAR images, azimuth velocity estimation derived the average offset of 1.65 m/s, while azimuth shift measurement derived that of 70.69 m with respect to AIS information. Ensuing application of this study includes moving target indication especially in ground regions.

Vertical land motion (VLM) is an essential information for relative sea level changes measured by tide gauges. Corrections to tide gauge records for VLM will provide information to understand the regional and global sea level changes. In this study, time-series SBAS-InSAR algorithm was implemented to measure the ground subsidence/uplift at tide gauges located in the Korean peninsula using Sentinel-1 A/B SAR data. We processed about 10 tide gauges in Korea and observed VLM at Pohang, Incheon and Jeju tide stations.

This paper suggests a novel and reliable method to detect water body from Sentinel-1 SAR satellite data using deep learning technique. There have been a lot of studies to extract water body from SAR images with deep learning. Although they achieved good performance, most of them used training data without guaranteeing good quality. In this study, land cover map generated by an official government agency were used for labelling ground truth data. After identifying the acquisition date of aerial photo used for generating the land cover map, vector polygons for river or reservoir were extracted and used as label data. This new method reduced producing time and cost to generate reliable training data. After training our deep learning model, it showed 0.874 of f1score. We also tested our deep learning model to the heavy rain season in Korea (August 2020) and successfully detected river flooding.

Monitoring of tidal creeks is important for safety in tidal flats and for understanding coastal changes due to sedimentation and erosion. The tidal creek is a small stream developed on tidal flats and requires very precise Digital Elevation Model (DEM) to detect it. Interferometric SAR (InSAR) technique was usually applied for generating DEM over inland regions. In tidal flat regions, low backscattering due to flatness and remnant water of the tidal flat prevents from generating high precision DEM. In this study, optimum InSAR baseline condition was investigated to generate such a precise DEM in the tidal flat. The long-baseline TanDEM-X and airborne InSAR system were found to be useful, and the tidal creek information (width and depth) was successfully extracted from the DEM that was generated from optimal baseline InSAR systems.

Monitoring and detecting ships via machine learning based algorithm were regarded efficient in martial and economic manners. As an algorithm regarding automated training data retrieval from SAR image was proposed, the identification of unclassified ships without AIS information could be raised as another challenging issue of ship surveillance. This study presented the effective identification algorithm of discerning unclassified ships from AIS information and the results of conventional ship detection based on machine learning. The accurately detected ships were selected from the conventional ship detection results, followed by the preprocessing of AIS information corresponding to the SAR images containing the detection results. Superposition of AIS information on accurate detection results was conducted and concluded the ships without AIS information as unclassified ships. From 3 Sentinel-1 SAR images, it obtained the average rate of identification as 85.67%. Additional research implementing the identification algorithm accompanied by rapidly acquired satellite or airborne SAR images could be effective in rendering a ship surveillance system with rapid response.

Ship detection in coastal regions accompanied by machine learning could be effective in economic and martial issues. However, conventional researches on ship detection mainly focused on modifying the training model itself instead of obtaining qualified training data. In order to ameliorate the training data in the aspect of quality and quantity, this research aims to directly obtain training data from AIS information. From discrete AIS information, interpolation on SAR acquisition time was conducted, followed by adjusting the Doppler frequency shift caused by each vessel's velocity. Training data was constructed from the adjusted position using internal location of AIS sensor on each type of vessel. Extracted training data by the proposed algorithm from Sentinel-1 images was tested with CNN model. As the detection performance of the extracted training data exceeded that from visual interpretation, this study concluded that qualified training data could be extracted from the proposed algorithm.

Coastal sea level changes measured by tide gauges are affected by the severity of coastal land motion. Subsidence due to natural and anthropogenic activities in the coastal regions were directly exacerbates the tide gauge records. In this study, we aim to monitor the coastal land motion at the tide gauges in Korean peninsula using multi-temporal InSAR techniques. We present the methodology that simply employs the selection of sequential and small temporal interferograms in the StaMPS-SBAS technique for increased coherent PS pixels over coastal regions. C-band Sentinel-1 SAR data from ascending and descending mode were acquired over Pohang and Yeosu regions to map the coastal land motion. The preliminary results showed significant increased number of initial PS pixel density than conventional StaMPS-SBAS technique, chosen by Amplitude-Dispersion Index (ADI) and revealed the subsidence about 2 cm/year in Pohang tide gauge and small uplift of 1.46 mm/year near Yeosu tide gauge.

Monitoring land subsidence is important to prevent and manage disasters. The extent of land subsidence can be measured by various ways and one of them is PSI technique. Guatemala City has geologically weak structure and suffered from disasters such as sinkhole and landslide. The purpose of this study was to measure wide range of relative movement in Guatemala City and to analyze obtained values. C-band Sentinel-1 A/B data during November 2014 to March 2018 were used in this study. By gathering information from both ascending and descending paths, relative movements were decomposed from line of sight of each path to vertical and horizontal component. As a result 4 regions in Guatemala City showed significant subsidence patterns with average vertical movement going down with a scale of 14 mm/year.

Coastal Subsidence is a global phenomenon that caused by both natural and anthropogenic activities. The impact of coastal subsidence can affect the monitoring of sea level rise by in-situ tide gauges. The Korean peninsula is one of such areas that reported considerably rapid sea level rise than the global average in the recent years. The objective of this study is to estimate the earth's surface movements along the coastal areas of Pohang, South Korea. To achieve that, we acquired C-band Sentinel-1 data during 2016 - 2017. Time-series InSAR technique is applied to measure displacements at high-resolution with reduced decorrelation noise from interferograms. The results showed about 1.83 cm/year subsidence in the Pohang coastal area, where significant sea level rise is observed in this region. The results will provide further insight into the localized subsidence rates in the study sites and its impact on the sea level monitoring.

In this study, we investigated signal processing algorithm to reconstruct SAR image obtained from Frequency Modulated Continuous Wave - Synthetic Aperture Radar (FMCW-SAR). The airborne SAR raw data is usually obtained with continuous motion and non-linear (fluctuating) flight path. In order to compensate these effects, Back-Projection Algorithm (BPA) which can compensate the continuous motion effect was developed in this study. The algorithm was evaluated by applying it to the simulated and real-airborne SAR data.

This study aims to monitor the long term behavior of the bridge using time series interferometric technique. The accurate interpretation of the interferometric phase in the case of bridge, relies on the understanding of contributions of components such as DEM error, thermal expansion, and linear displacement. To decompose each contribution, this study applied the PSI algorithm using multi-temporal Cosmo-Skymed SAR data for Kimdaejung bridge. This study was able to find the unique displacement pattern, which is called as a deflection. The deflection is commonly caused by the creep, shrinkage, and ageing. Due to the early stage after the completion of the construction, the physical change of the bridge characteristics leads to the deflection. Thus, this paper verifies the potential to monitoring the long term deflection and implies that the bridge need to be monitored continuously.

In recent years, subsidence due to excessive groundwater withdrawal is a major problem in the Kathmandu Basin. In addition, on 25 April 2015, the basin experienced large crustal displacements caused by Mw 7.8 Gorkha earthquake. Consequently, the effects of co-seismic deformation could affect the temporal and spatial nature of anthropogenic subsidence in the basin. In this study, we applied Small Baseline subset (SBAS)-DinSAR technique to estimate the spatio-temporal displacements in the basin, before and after the earthquake. 16 ALOS-1 L-band and 20 Sentinel-1 C-band SAR data are used for measuring pre- and post-seismic subsidence velocity, respectively. We found that the subsidence velocity in the central part of the basin is increased after the mainshock.

This paper presents a new damage-mapping algorithm based on coherence images estimated from multitemporal polarimetric-interferometric synthetic aperture radar (SAR) data. The interferometric coherence has been restricted in the conventional damage-mapping approaches because the decorrelation sources are too complicated to interpret accurately and temporal decorrelation effects caused by slowly occurring natural changes and disaster events are often coupled together. To overcome these limitations, we formulate a coherence model that accounts for temporal decorrelation in two simplified layers, ground and volume layers, for long-temporal repeat-pass scenarios with zero spatial baseline. The model parameters include: 1) ground-to-volume ratio, a factor to determine the relative scattering contribution of ground and volume layers; 2) temporally correlated change, which captures the exponentially decaying behavior of coherence with time; and 3) temporally uncorrelated change, which is associated with random temporal changes. We estimate the model parameters in three steps: coherence optimization, interferometric pair-invariant parameter estimation, and interferometric pair-variant parameter estimation. To isolate the effects of disaster events from background natural changes, we calculate the probability density functions of historical change pixel by pixel and produce a probability map of damage. We tested the algorithm with uninhabited aerial vehicle data acquired from 2009 to 2015 for mapping the area damaged by the 2015 Lake Fire in California. Based on performance evaluation using receiver operating characteristic curves for optimized coherences and averaged probability maps, the proposed method reduced the false alarm from 0.25 to 0.07 when the probability of detection was 0.85 compared to coherence products alone.

This study aims to evaluate the potential of coherent change detection using multi-temporal polarimetric interferometric SAR data. One of the limitations in damage area extraction is that the decorrelation caused by the disaster is commonly coupled with the natural changes. Also, the interpretation of the coherence is troublesome and requires the coherence model. The approach used in this study is based on the modified coherence model for a case of long-temporal and zero-spatial baseline. The inversion processes yield the temporal decorrelation contributions of ground and volume, respectively, having the physical meaning. We additionally applied simple statistical probability estimation method for the damage area to isolate the contributions of disaster from the natural changes. In this study, we used UAVSAR data acquired over Lake fire which occurred in June, 2015.

Intertidal flats which are located between land and ocean are productive and rapidly changing places. But these places are now facing many environmental challenges related to climate change and human-induced impacts. Topographic change related to sedimentation or erosion in intertidal flats is the most evident sign of the environmental changes. The intertidal flats usually have small topographic variations (less than 5m) and experience ebb and flood tides every day. Thus, the conventional SAR interferometric techniques (repeat-pass InSAR) cannot be applied for generating DEMs in these intertidal flats. In this study, we developed a long-baseline single-pass airborne interferometric SAR (InSAR) system that has small ambiguity height and collected airborne InSAR data in several intertidal flats, the west coast of Korean peninsula. The constructed topographies using the long-baseline airborne InSAR system were compared with TanDEM-X DEM obtained during long-baseline mission phase.

Irrigated rice fields in Asia have a distinct topography and scattering mechanisms. Because of these factors, many studies using radar backscattering information have been conducted to improve the mapping accuracy; however, relatively little attention has been paid to the topographic features (TF) provided by polarimetric synthetic aperture Radar (PolSAR) data. To address this issue, this letter presents a simple rice field mapping method in its late-vegetative stage using both the TF and scattering features (SF) of the PolSAR data. First, two TF, the polarization orientation angle and the dominant beta angle from the PolSAR data, were analyzed with respect to the slope from a digital elevation model. The results indicated that this feature pair had a moderately high correlation. As a result, flat areas were easily extracted using these two TF. Second, to use the SF of rice in the late-vegetative stage, the copolarized ratio and the copolarized phase difference were simply combined to map only paddy fields, of which scattering was dominated by the double-bounce scattering caused by the ground and canopy reflection. The results using C-band Radarsat-2 data were compared with a support vector machine classifier and demonstrated that the proposed mapping method provided an overall better performance. The proposed method might also be efficiently applied, with modification, for the aforementioned purpose in inaccessible areas.

TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement), a German SAR satellite, was launched in June 2010. Since 2012, TanDEM-X mission has provided the most detailed global DEM with unprecedented accuracy. Also, they carried out various experimental mode including long baseline XTI (cross track interferometry). Despite their progressive instrument calibration and high accurate orbit determination, the error of topographic height was still remained in the level of global DEM and was not entirely estimated. Particularly, the DEM accuracy in coastal area including tidal flats has never been reported. In this study, we evaluated the accuracy of TanDEM-X DEM in coastal area by comparing with RTK GPS measurements, and we investigated the optimal baseline condition to generate significant DEM in tidal flat.

Detection of changes induced by major events such as earthquakes, flooding and volcanic eruptions from interferometric SAR data is difficult due to the coupled effects with temporal decorrelation caused by natural phenomena such as rain, snow, wind and seasonal changes. In this study, we aim to separate the decorrelation caused by natural changes from the one caused by the major event by analyzing the coherence behavior using a temporal decorrelation model. We formulated the temporal decorrelation model that accounts for the random motion and dielectric changes. By applying the model into the multi-temporal coherence before the event, we extracted the temporal decorrelation components induced by natural phenomena. Based on the extracted parameters, their decorrelation probabilities related to natural changes were estimated in canopy and ground. The model parameters are also extracted from the interferometric SAR data acquired across the event. We compared probabilities between the natural phenomena and the certain event in order to assign the changed regions. Pixels with cumulative probabilities greater than 80% are selected as changed due to the event. A case study for detecting volcanic ash during the eruption of the Shinmoedake volcano in January 2011 was carried out using L-band Advanced Land Observation Satellite (ALOS) PALSAR data.

Intertidal flats are productive and rapidly changing places. However, these places are facing many environmental challenges related to climate change and human-induced impacts. Topographic change due to sedimentation or erosion in intertidal flats can be the key indicator for recognizing these environmental changes. The intertidal flats usually have small topographic variations (less than 5m) and high-moistured soil surfaces (greater than 50%). The conventional SAR interferometric techniques cannot be used for generating DEMs in these intertidal flats. In this study, we developed a long-baseline airborne interferometric SAR (InSAR) system that has small ambiguity height and collected airborne InSAR data in Jebu intertidal flat, west coast of Korean peninsula. The constructed topographies using the long-baseline airborne InSAR were compared with TanDEM-X DEM (acquired during long-baseline mission phase) and GPS-RTK measurements.

Grounding line estimation has substantial importance of determining vulnerability of ice mass in the Polar Regions. In this study, high resolution digital elevation model is utilized to estimate and update grounding line of Thwaites Glacier in West Antarctica in 2011. Estimation is done by comparing the calculated bottom topography of ice with the bathymetry model from Operation IceBridge. The bottom topography is computed by subtracting ice thickness which is also estimated from surface elevation assuming hydrostatic equilibrium. The updated grounding line infers its retreat of up to 5km inland when compared with the grounding line in 2009. The retreat is conspicuous over the bathymetric trough. Conceivable error sources are still present such as ice column density, bathymetry accuracy and firn height. Nonetheless, high resolution digital elevation model shows high practicability of estimating grounding line.

Change detection using synthetic aperture radar (SAR) data is useful in emergency situations and unfavorable weather conditions. In this letter, change detection using multitemporal polarimetric Uninhabited Aerial Vehicle SAR data is investigated in an urban environment. The most robust polarimetric parameters are determined, and change detection techniques using a maximum likelihood ratio and a hyperbolic tangent model function are applied to the selected parameter. The model function was introduced to quantify the change characteristics and to rule out seasonal changes or those related to mobile features, and thus to only detect durable and permanent changes in urban environments. A comparison of results with historical Google Earth images showed a good level of agreement. Fitting of the hyperbolic tangent function to the multitemporal polarimetric parameters significantly reduces the false detection rate and indicates whether a building was constructed or destroyed, as well as when the detected changes occurred.

Detection of changes caused by major events-such as earthquakes, volcanic eruptions, and floods-from interferometric synthetic aperture radar (SAR) data is challenging because of the coupled effects with temporal decorrelation caused by natural phenomena, including rain, snow, wind, and seasonal changes. The coupled effect of major events and natural phenomena sometimes leads to misinterpretation of interferometric coherence maps and often degrades the performance of change detection algorithms. To differentiate decorrelation sources caused by natural changes from those caused by an event of interest, we formulated a temporal decorrelation model that accounts for the random motion of canopy elements, temporally correlated dielectric changes, and temporally uncorrelated dielectric changes of canopy and ground. The model parameters are extracted from the interferometric pairs associated with natural changes in canopy and ground using the proposed temporal decorrelation model. In addition, the cumulative distribution functions of the temporally uncorrelated model parameters, which are associated with natural changes in canopy and ground, are estimated from interferometric pairs acquired before the event. Model parameters are also extracted from interferometric SAR data acquired across the event and compared with the cumulative probabilities of natural changes in order to calculate the probability of a major event. Subsequently, pixels with cumulative probabilities greater than 75% are marked as changed due to the event. A case study for detecting volcanic ash during the eruption of the Shinmoedake volcano in January 2011 was carried out using L-band Advanced Land Observation Satellite PALSAR data.

Doppler velocity can be derived by calculating Doppler shift anomalies between predicted and estimated Doppler centroids. The predicted Doppler centroid is calculated based on a geometric model of satellite assuming that the target is not moving. The estimated Doppler centroid can be directly extracted from the raw SAR signal data by applying the average cross-correlation coefficient method. It is known that wind-generated ocean waves can significantly contribute to Doppler velocity due to the correlation between orbital motions of the waves and (tilt and hydrodynamic) modulated radar cross sections, in addition to what sea surface current contributes. In this study, the characteristics of Doppler velocities under hurricane conditions were investigated using RADARSAT-1 ScanSAR raw data. Five different hurricanes (Hurricane Dean, Hurricane Ivan, Hurricane Kyle, Hurricane Lili, and Typhoon Xangsane) and sequential acquisitions of two hurricanes (Hurricane Kyle and Hurricane Lili) were selected to study the contribution of wind-induced waves to Doppler velocities and compared with in situ measurements of drifting buoys. The results show that hurricane-generated seas and associated winds and waves appear to be different from ordinary sea state. This leads to lower estimates of Doppler velocities than expected and much closer to sea surface current velocities.

SAR change detection is useful when emergency situations occurred and weather conditions are unfavourable. In this study, a change detection using multi-temporal polarimetric UAVSAR data was investigated in urban environment. The most robust polarimetric parameter was evaluated, and change detction techniques using maximum likelihood ratio and hyperbolic tanget function were applied to the selected parameters. The comparison results with Google Earth's historical images showed a quite good agreement. A fitting of hyperbolic tangent function to the multi-temporal polarimetric parameters much reduced the false alarm rate, and it also provide whether the building was constructed or destructed and when the the changes occurred.

TanDEM mission enables the generation of high resolution digital elevation model over fast changing surfaces such as the Antarctic ice sheets. This study assesses the surface elevation over the marginal region of Thwaites Glacier in West Antarctica. The absolute height of SAR interferometry was derived by utilizing CryoSat-2 radar altimeter over the same region with minimizing temporal differences between the datasets. Then, the derived heights are compared with the airborne laser and radar altimetry data from the Operation IceBridge. The results show that surface and coherence are two main factors causing height offsets between the datasets, however, each effect must be separated for more accurate analysis.

The coherence in SAR interferometry is used as a parameters for change detection. However, it is difficult to quantitatively analyze the coherence value because the decorrelation come from diverse reasons and the unwanted targets naturally changes. In order to isolate the changed region by an accumulation of the volcanic ash, we analyze the reasons of the decorrelation. We estimate the temporal decorrelation using time-series dataset and predict the temporal decorrelation for dataset involving dramatic changes. The estimated and the expected ratio is useful to identify the regions affected by volcanic ash. This method successfully isolate the regions with dramatic change due to volcanic ash from the naturally changing region, easily misinterpreted as a dramatic changed area.

The coastal sea surface temperature (SST) is one of the important oceanic environmental factors in determining the change of marine environments and ecological activities. In order to extract coastal SST, we developed ship- and airborne thermal infrared remote sensing systems. The acquired thermal infrared images are radiometrically calibrated using an atmospheric radiative transfer model as well as a thermometer/hygrometer sensor and geometrically calibrated using a GPS/IMU sensor. In this study, the ship- and airborne SST data were compared and verified with spaceborne SST data and in-situ data. The comparison and validation results showed that the ship and air-borne thermal infrared remote sensing could be used in generating SST map, especially in mapping fine-scale coastal features.

The surface heights of the marginal region of ice sheets are of interest among most glaciologist and remote sensing studies of glaciers. There have been a number of difficulties in estimating accuracy surface information in the marginal regions due to rough surfaces and high slope angles. In this study, the surface heights of the marginal region of Thwaites Glacier are calculated in high resolution using SAR interferometry of TanDEM-X. The calculated surface heights are compared after the absolute offset correction using CryoSat-2 radar altimeter. The results show that the general surface patterns are highly correlated, however, the RMS heights are larger than expected. The possible explanations can be related to the footprint size of CryoSat-2 and snow penetration depth due to different radar frequencies. Besides, absolute phase offset needs to be calculated accurately. In general, the possibility of utilizing TanDEM-X and CryoSat-2 data in the marginal regions is expected to be promising.

One of the main limitations in measuring ground deformation using synthetic aperture radar interferometry (InSAR) is atmospheric phase delay effects. In volcanic regions, the atmospheric phase delay effects can cause serious problems in detecting volcanic unrest because atmospheric thickness is inversely related with the elevation of a volcanic mountain. It is commonly known that the atmospheric phase screen (APS) can be decomposed spatially into stratified and turbulent components. In this paper, the stratified and turbulent atmospheric conditions of a volcanic area were simulated using weather research and forecasting (WRF) model, and the simulated atmospheric conditions were compared with in situ radiosonde data. The comparison results proved that the stratified APS from the WRF model could reflect the reasonable patterns of seasonal changes and vertical profiles with dependable quality. We also found that the stratified APS was significantly correlated with time and sometimes severely contaminated the quality of volcanic deformation estimation. These results indicate that the temporal high-pass (HP) filtering, which has been usually applied in time series InSAR analysis for extracting and removing APS, cannot work properly in volcanic area. Thus, we propose a new method that employs the stratified APS obtained from the WRF model by correlating with topography and the (residual) turbulent APS that can be effectively eliminated by temporal HP filtering in persistent scatterer interferometry (PSI). We applied the proposed method (atmosphere-corrected PSI) to Advanced Land Observing Satellite Phased Array type L-band SAR data that cover Shinmoedake volcano, Japan, and found that the estimated surface deformation and APS agreed well with those measured from GPS and Moderate-Resolution Imaging Spectroradiometer data, respectively.

One of difficulties in measuring the ground deformation using SAR interferometry is the atmospheric phase screen (APS). Severe stratified APS could occur in mountain region. Thus APS could compromise the accuracy of the ground deformation estimated by SAR interferometry in stratovolcano. Since the stratified APS is correlated with the elevation, both ground deformation and APS error are indistinguishable. In order to improve the accuracy of the ground deformation and estimate the accurate APS involved in SAR interferogram, the effective algorithm is necessary which reflect the properties of APS in space and time. In this research we proposed a time-series approach combined with the weather prediction model. The main concept about APS is that the severe stratified APS usually occur in mountain region correlating with elevation and has the seasonality, while the turbulent APS has a random process in time and space. On basis of these temporal and spatial properties, we applied the troposphere-corrected time-series interferometry and successfully extracted both APS and estimated the better ground deformation results than a conventional time-series analysis.

Sea surface current plays an important role in understanding physical oceanography and can provide critical information for preventing natural disasters such as oil spill and ship distress. In this study, two techniques using space-borne Synthetic Aperture Radar (SAR) systems were processed to evaluate their feasibility to extract sea surface current velocities. The first technique employs the Doppler shift algorithm from a single SAR system which is generated when moving targets are imaged in East Sea of Korean Peninsula. The second technique utilizes Along-Track Interferometry (ATI) from dual SAR system to estimate sea surface current velocity in Strait of Korea. Envisat ASAR and TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) data were used in each technique, respectively.

Tidal flat plays an import role in purifying pollutants discharged from land and in providing marine products such as oyster and many kinds of shellfishes. Oyster in tidal flat usually has rough structures, causing a strong depolarization in microwave backscattering. The signatures can be detected using multi-frequency polarimetric synthetic aperture radar (SAR) data. Strong volume and depolarization effects were observed in the oyster reefs of C-band SAR data, while only surface scattering was dominant in the L-band SAR data. We also investigated why the oyster reefs distributed in those areas and how they generated naturally. We focused on groundwater discharge to relate the distribution of oyster in tidal flat, and found obvious signatures of groundwater discharge along the marginal part of the tidal flat from SAR data.

Melt pond statistics (size and shape) have previously been retrieved from aerial photography and high-resolution visible satellite data. These submeter- or meter-resolution visible data can provide reasonably accurate information on melt ponds, but are greatly constrained by the limited solar illumination and frequent cloud cover in the Arctic region. In this study, we venture into exploring high-resolution synthetic aperture radar (SAR) or imaging radar method for melt pond mapping, which is not severely disrupted by cloud or low solar zenith angle. We analyzed high-resolution airborne SAR images (0.3-m resolution) of midsummer sea ice, acquired from a helicopter-borne SAR system in the northern Chukchi Sea. The pond area and shape (circularity) derived from the airborne SAR images showed that the statistics were comparable to those previously observed from aerial photographs. We argue that high-resolution SAR, together with one-to-one comparison with coincident aerial photographs, can be used to map melt ponds at a level of detail comparable to aerial photography or high-resolution optical satellite remote sensing. Our encouraging results suggest the possibility of using high-resolution SAR (current or future systems) to map melt ponds in the Arctic region.

The atmospheric phase delay when microwave propagates in troposphere can be severe and be misinterpreted as a surface deformation in differential SAR interferometry (DInSAR). The atmospheric phase delay errors are main difficulties in measuring the deformation caused by volcanic activities. In order to minimize the effects of atmospheric water vapor we calculated mesoscale atmospheric phase delay effects in DInSAR from the various atmospheric data such as WRF model, MODIS products, and radiosonde data. In addition, time-series analysis with atmospheric correction technique was carried out. We successfully minimize the atmospheric phase delay effects, misinterpreted as deformation, and measured the ground motion on the Shinmoedake volcano.

Various polarimetric analyses were carried out in order to verify the break-up of the Thwaites Ice Tongue. The hypothesis was that the observed crack on the ice tongue must be filled with sea ice if completely broken. Although the recently published paper asserted that the ice tongue had been completely broken, we found that it is too early to come into the conclusion. The depolarization ratio is mainly used to compare and distinguish between certain features of the ice surface and entropy, as well as mean alpha angle, is also computed as complementary information.

In this paper, we have investigated the relationship between the depolarization effects and the wintertime sea ice thickness in the landfast ice region where smooth thick first-year ice (FYI) and deformed old ice coexisted by using C- and X-band spaceborne polarimetric synthetic aperture radar (SAR) data (RADARSAT-2 and TerraSAR-X). We have found a strong correlation between the in situ sea ice thickness and the SAR-derived depolarization factors (copolarized correlation and cross-polarized ratio). The observed relationships have demonstrated not only a categorical difference between FYI and multiyear ice (MYI) but also a one-to-one continuity in the scatter plots, rather than being clustered. It clearly shows that the observed correlations are not merely from the categorical difference in scattering mechanism between FYI and MYI and that there might exist a one-to-one relationship between thickness and depolarization factors at least in our deformed ice case. This suggests that depolarization factors could be effective SAR parameters in the estimation of wintertime sea ice thickness. Numerical model simulations explained some portions of the correlation by employing multiple scattering on the sea ice surface and volume scattering within the low-density subsurface layer.

Polarimetric signatures of oyster reefs in tidal flats were investigated using multi-frequency polarimetric SAR data. The scattering mechanism in tidal flats was analyzed by the Freeman-Durden target decomposition and the target depolarization effect was quantitatively measured using the cross-polarized ratio. Strong multiple scattering and depolarization effect were observed in oyster reefs areas from Cand X-band data. These scattering signatures were also verified from in-situ measurements using a ground-based polarimetric scatterometer system. However, no difference was observed between the scattering signatures of oyster reefs and mud flat areas from L- band data, which have a relatively longer wavelength. These results suggest that multi-frequency polarimetric SAR observations can be used to quantitatively detect the spatial distribution of oyster reefs in tidal flats.

Space-borne SAR (Synthetic Aperture Radar) has been one of the most effective tools for physical oceanographic and air-sea interface research. We applied the effect of Doppler shift observed in the raw SAR data to estimate ocean surface velocity. Relative motion between sensor and Earth's target causes Doppler shift of radar signal which can be recorded in SAR signal data. The relative motion can be derived by comparing the difference between target Doppler centroid and reference Doppler centroid. The reference Doppler centroid is a Doppler centroid that corresponds to the stationary area or a fixed target. The reference Doppler centroid can be calculated with the geometry model on the rotating Earth. On the other hand, the target Doppler centroid was estimated by applying the correlation Doppler estimator to the RADARSAT-1 ScanSAR raw data (also known as the Average Cross Correlation Coefficient; ACCC). Target Doppler centroid is caused by the movement of upper ocean waterbody. By comparing of these two Doppler centroids, line-of-sight velocities of ocean surface can be retrieved. Until recently, studies to characterize and quantify oceanic response to tropical cyclones such as typhoon and hurricane have rarely reported because of its difficulty of observation under these extreme conditions. The object of this study is to extract more accurate ocean surface velocity response to tropical cyclones from RADARSAT-1 ScanSAR raw data. In the future, we will be able to investigate oceanic response to tropical cyclones from the estimated ocean surface velocities.

In this study we investigated the relationship between the depolarization effects and winter-time sea ice thickness in the landfast ice region where smooth first-year ice and deformed old ice co-existed, by using C- and X-band polarimetric satellite SAR data. Acquisitions of high-resolution SAR data with multiple satellite platforms (TerraSAR-X and Radarsat-2) were coordinated during a sea ice field campaign examining landfast sea ice on the coast of northern Greenland. We describe a sensitivity study of depolarization factors (co-polarized correlation and cross-polarized ratio), which can be used to estimate the degree of the depolarization effect using extended Bragg model. We found a strong correlation between ice thickness and the depolarization factors, suggesting that depolarization factors can be effective parameters in the estimation of sea ice thickness, particularly in an area where thick FYI and MYI co-exit.

Significant amount of research and development is being directed on monitoring activities of daily living of senior citizens who live alone as well as those affected with certain disorders such as Alzheimer's and Parkinson's. A combination of sophisticated inertial sensing, wireless communication and signal processing technologies have made such a pervasive and remote monitoring possible. Due to the nature of the sensing and communication mechanisms, these monitoring sensors are susceptible to errors and failures. In this paper, we address the issue of identifying and isolating faulty sensors in a Body Sensor Network that is used for remote monitoring of daily living activities. We identify three different types of fault isolation strategies and propose both history-based and non-history based approaches.

In this study, we derived the relationship between target depolarization factor and physical parameters of sea ice in order to estimate the thickness using dual-polarization C-and X-band space-borne Synthetic Aperture Radar (SAR) data. The target depolarization factor, the cross-polarized ratio of C-band SAR data, which can explain the target depolarization effect, were strongly related to changes in surface roughness of thick First-Year Ice (FYI) and Multi-Year Ice (MYI), and were almost insensitive to variations in the surface dielectric constant of thick FYI and MYI and the incidence angle of C-band SAR data. This relationship showed a high correlation between target depolarization factor of C-band SAR data, the cross-polarized ratio, and thick FYI and MYI thickness. We validated the estimated method using RADARSAT-2 and TerraSAR-X data and ground-truth data acquired in the Arctic Sea off the northern coast of Greenland.

In this paper, we discussed the functionalities of KOMPSAT-5 from the emergency response perspective to oil spill detection, describing its technical abilities in terms of maneuverability, system response time, image quality, and damping ratio. The damping ratio at the X-band was higher than that at other available frequencies, which increased the probability of oil spill detection from SAR data. However, the detection of oil spills depends significantly on the wind condition and the presence of look-alikes. Oil spill detection algorithm for KOMPSAT-5 estimates wind information directly from SAR data, and is expected to enhance the detection capability.

RADARSAT-2 is a follow-up to RADARSAT-1 and is an all weather Earth observation satellite with fully polarimetric imaging capability. The synthetic aperture radars (SARs) onboard both RADARSATs are C-band imaging radars and they are well suited for Earth's ecosystem monitoring and maritime surveillance, because of the near polar orbit and their unique all weather imaging capability, independent of solar illumination. In this paper, RADARSAT-2 is first introduced and several applications of various modes of SAR data to coastal zone problems are discussed, including the coastal surface wind, waterline mapping, and polarimetric SAR data inversion for topographic and geological parameters of tidal flats. Coastal zones, the important interface between the land and the ocean, where a large proportion of the world's population inhabits, continuously change and evolve. The dynamic interaction of coastal winds, coupled with the coastal waves and currents, continuously erode rocks and land mass, and move and deposit various sediments on a continuous basis, along with the tides. Estimation of wind speeds and directions in coastal areas are empirically formulated and can further be improved with the available fully polarimetric data from RADARSAT-2. The water line mapping critically depends on the SAR frequency, or the wavelength of the SAR data used, and RADARSAT-2 SAR data using C-band should map waterlines more accurately than the longer wavelength L- or P-band SAR systems. The roughness parameters and partial information on the tidal flat compositions can be obtained from fully polarimetric SAR data. Some results obtained from NASA AIRSAR(2000) L-band data and RADARSAT-2(2008) C-band data do not fully agree with field measurements and further investigation is in progress. The inversion of polarimetric SAR data is a very complex problem and critically depends on the SAR signal frequency and model functions. RADARSAT-2 is an imaging radar, which is very flexible and powerful tool for potential coastal zone applications. Key RADARSAT-2 features and potential coastal zone application capabilities are also briefly reviewed.

Synthetic aperture radar (SAR) signals can propagate through hazardous weather and atmospheric conditions with heavy cloud cover, volcanic dust, snow, or rain. The all-weather capabilities of SARs have attracted significant interest in remote sensing communities, since serious environmental disasters such as oil spills have been highly ¿elusive¿ to optical sensors, making visible spectrum data vulnerable to rapidly changing atmospheric conditions. In this paper, we discuss the technical functionalities of TerraSAR-X from the emergency response perspective, describing its technical abilities in terms of a damping ratio, radiometric accuracy, and noise level with reference to the actual Hebei Spirit oil-spill incident that occurred on the west coast of the Korean peninsula in December 2007. The damping ratios estimated from the TerraSAR-X data as a function of Bragg wavenumber for various wind speeds indicate that TerraSAR-X data can be effectively used to identify oil-spill areas with acceptable accuracy. We also received ERS-2, ENVISAT, RADARSAT-1, and ALOS PALSAR data for this oil-spill event, not simultaneously but with varying time delays. The processing results for the multitemporal data sets obtained from the X- and C-band SAR systems are useful since they can be used to determine the near-real-time migration of spilt oil. The results of the current study indicate that there are distinct advantages of using X-band TerraSAR-X data for oil-spill detection compared to the data obtained at other available frequencies.

The tidal flats are largely developed along the western and southern coast of Korean peninsula. The simulation results of tidal flats through analytical scattering models could not be fully assessed by using only surface roughness (RMS height and correlation length) and soil moisture. The tidal flats are usually saturated with saline sea water and partially covered with remnant water in depressions. This may cause lower backscatters in SAR images due to the partial specular reflection.

Recently, Body Sensor Networks (BSNs) are being deployed for monitoring and managing medical conditions as well as human performance in sports. These BSNs include various sensors such as accelerometers, gyroscopes, EMG (Electromyogram), EKG (Electro-cardiograms), and other sensors depending on the needs of the medical conditions. Data from these sensors are typically Time Series data and the data from multiple sensors form multiple, multidimensional time series data.

The coastal zones of the Korean peninsula are well known for their large tide ranges and vast expanse of intertidal flats. In this paper, methods of extracting the roughness of the scattering surface of intertidal mudflats from polarimetric synthetic aperture radar (SAR) data have been investigated. The L-band NASA/Jet Propulsion Laboratories airborne SAR data, which were acquired in the intertidal zone during PACRIM-II Korea campaign, were used to estimate the roughness of intertidal mudflats. Surface roughness can be utilized as a useful parameter to monitor the fishery activities in intertidal flats as well as the changes in textural characteristics of surface sediments. In order to retrieve roughness parameters, such as the rms height and the correlation length, of intertidal mudflats, three types of roughness inversion algorithms, based on the Integral Equation Method (IEM), semiempirical, and extended-Bragg models, have been investigated and developed. The inversion algorithms based on the IEM and semiempirical models can be applied to the dual-polarized SAR, while the extended-Bragg model-based inversion approach is also applicable to the fully polarimetric SAR observations. Results indicate the fully polarimetric approach is more pertinent to monitor geophysical parameters from space than the dual polarimetric approach, even if it is possible to reduce the number of unknown surface variables in the specific case of inversion problems.

On December 7th, 2007, more than 10,000 tons of crude oil from a tanker leaked into the Yellow Sea off the west coast of Korean Peninsula. Several SAR images including TerraSAR-X, ENVISAT ASAR, RADARSAT-1, ERS-2 SAR and ALOS PALSAR were acquired over the contaminated area from oil spill. Observed dark patches in SAR images, due to the presence of oil slicks, were extracted using adaptive thresholding methods, and then classified based on field information. The damping ratio, which is a ratio between slick-free and slick-covered sea surface, was analyzed by the SAR radar frequencies and wind condition. With the acquired SAR images in temporal resolutions, the movement of oil slicks was monitored and traced.

A new method for estimating the atmospheric transmittance and wind speed over the ocean from WindSat data is derived using a simplified model for the ocean surface reflectivity. The simplified reflectivity model is used to calculate both the surface emissivity and the reflection of downwelling atmospheric radiation. The wind-speed dependence of the surface reflectivity is parameterized using simple rational functions with coefficients determined from the WindSat data. Because the vertically polarized brightness temperature depends primarily on the atmospheric state, it is used to obtain an initial estimate of the atmospheric transmittance at each spatial location. These estimates are then combined with the horizontally polarized brightness temperature to estimate the wind speed at each location. The first wind-speed estimate is used to refine the estimate of the transmittance, and the process is repeated until the estimates converge, resulting in a simultaneous solution for the atmospheric transmittance and the wind speed. The results are illustrated for two WindSat data sets collected on September 12 and 14, 2003. We have also investigated two methods of estimating wind direction using WindSat measurements of the third and fourth Stokes parameters. The first method involves an algebraic solution for the wind direction from simultaneous measurements of the third and fourth Stokes parameters. The second method involves measurements of the third Stokes parameter from two look directions (fore and aft scan angles), made possible by the conical scanning geometry of WindSat. A comparison and evaluation of these methods is made using the same data sets.

Waterline detection in the intertidal areas was investigated through synthetic aperture radar (SAR) images and field measurements. Two valuable facts were found in this letter: 1) A discrepancy of waterlines between L- and P-band airborne SAR images was discovered and investigated through precise global positioning system measurements and the theory of the SAR imaging mechanism. In the intertidal areas having low slopes, the Bragg waves resonant with the radar signal can reside in different depths depending on the radar frequency, with the result that the boundary between water and land can be mapped differently in the respective SAR images. 2) Intertidal areas covered with a water film present low radar backscatter in SAR images, which can cause mislocation of waterlines

An efficient method for estimating atmospheric transmittance is presented for WindSat polarimetric microwave radiometer data. The method is dependent on an approximation that is valid for earth incidence angles near 53deg. By combining the estimated atmospheric transmittances at different frequencies, the water vapor and cloud liquid water can be retrieved. The method can also be used for making atmospheric corrections in order to improve wind vector estimates from WindSat data.

Tidal flats form a unique ecosystem, providing the habitat for various species of fauna and flora which play an important role in the essential function of water purification. Tidal flats have particular significance for coastal zone environmental assessment. This study aims to derive physical properties and surface characteristics of tidal flats from L-band NASA/JPL fully polarimetric AIRSAR data which was acquired over Southern coast of Korean peninsula in September, 2000, as part of the PACRIM-Ⅱ mission. Surface roughness is an essential parameter prescribed by surface sand ripples in tidal flat. We employed the Extended-Bragg (X-Bragg) model to measure the surface roughness on tidal flats from polarimetric SAR data. There are two approaches of X-Bragg model, which are circular polarization roughness and Anisotropy methods. The circular polarization coherence RRLL ρ was developed to investigate terrain surface roughness. Anisotropy method was developed to separate the surface roughness from surface dielectric constant by utilizing the scattering entropy H, the anisotropy A and the alpha angle The surface roughness was calculated and analyzed using the polarimetric anisotropy and the circular polarization coherence, RRLL ρ . I. INTRODUCTION ρ and the related polarimetric decomposition parameter, the anisotropy, was performed. In this study, the surface roughness parameters were estimated using these two methods which are based on the relation with circular polarization coherence and surface roughness. Four study sites in the intertidal study area in the Southern coast of Korean peninsula were selected for this study. The SAR data analyzed in this paper is multi-look images (MLC), acquired by the fully polarized NASA/JPL Airborne SAR (AIRSAR) survey during the PACRIM-Ⅱ campaign.

A numerical scattering model for artificial metal structure based on physical optics approximation is developed to identify the height of phase center, and the result is compared with interferometric SAR DEM. The interferometric SAR data were gathered by AIRSAR during PACRIM-Ⅱ campaign on Jeju Island. Power transmission towers on piedmont pasture along the slopes of Mt. Halla look like elliptic risings in TOPSAR DEM. The heights of risings are quantitatively analyzed using a scattering model in the way of achieving the height of phase centers of power transmission towers. A numerical algorithm is developed on the basis of physical optics approximation. The structure of power transmission tower was decomposed into hundreds of rectangular metal plates, of which the scattering matrix is known in analytic form, and the calculated scattering fields were summed coherently. The effect of direct backscattering component, ground-scatterer component and scatterer-ground component are decomposed and computed individually for each rectangular metal plate. The Δk- radar equivalent was used to calculate height of phase center of the scatterer. The heights of a selected power transmission tower and scattering algorithm results give existence and location of the transmission towers but not actual tower heights.

Discrepancy of waterline extracted from L- and P-band airborne SAR images was investigated in the intertidal zone through the field measurements and theory of SAR imaging mechanism. In the intertidal zone which has low slope, the Bragg waves resonant with each radar frequency can reside in different depth of surf zone, resulting in the boundary between water and land can be mapped differently in SAR images. I. INTRODUCTION Most large modern cities are located in coastal zones. It is essential to map and monitor changes in waterlines in a timely. Airborne or space-borne synthetic aperture radar (SAR) image provide an efficient aid to monitor and map the waterlines due to high resolution synoptic views of water extent. The west and south coast of the Korean Peninsula is famous for its large tidal range and vast tidal flats. Continuous observation of waterline is highly required in these areas. The extraction of waterlines from SAR images has been studied (1-5). Most of these studies were based on gray level thresholding, segmentation or tracing along the boundary between land and water directly shown in SAR images. However, images of waterlines generated by SAR suffer from a number of speckle effect and meteorological conditions. The return from the wind roughened and wave modulated water can frequently equal or exceed the return from a nearby land area, resulting in inadequate contrast for unambiguous separation. Furthermore, waterline itself estimated from SAR images can vary depending on the sea surface states. Radar frequency or wavelength is important parameter in ocean SAR imaging. Different radar frequency of signal will interact with different length of ocean waves - Bragg scattering (6-8). In the coastal region, the water depth is shallow and rapidly changing, and surface waves can feel the bottom, causing breaking waves. The interaction of these breaking waves with radar signals is extremely complex and very difficult to model. The means by which waves break depends on the nature of the bottom and the characteristics of the wave. For very mildly sloping beaches like tidal flat, typically the waves are spilling breakers - numerous waves occur within the surf zone and these waves begin to lose their energy through friction with the bottom (9). In this paper, we investigate the characteristics of (breaking) surface waves in surf zone in order to explain the discrepancy of waterlines extracted from multiple frequency SAR data in intertidal zone.

Ocean surface current information is one of the important factors which are employed for a variety of scientific pursuits especially on ocean environment. Although remote sensing techniques have been developed up to now, the investigation of ocean surface current using synthetic aperture radar (SAR) is not easy of access. This paper presents the results of ocean current observation using ERS-1 raw SAR data which were obtained off the coast of Jeju Island. We extract the ocean current based on the concept in which Doppler frequency shift and the ocean current are closely related. Moving targets cause Doppler frequency shift of the backscattered radar radiation of SAR, thus the line-of-sight velocity of the scatters can be evaluated. The Doppler frequency shift can be measured by estimating the difference between Doppler centroid obtained and reference Doppler centroid calculated. Theoretically, the Doppler centroid is zero, however, squinted antenna which is affected by several physical factors causes Doppler centroid to be nonzero. The Doppler centroid can be estimated from measurements of sensor trajectory, attitude and Earth model. By compensating ERS attitude errors, we could enhance Doppler centroid accuracy and verify that the extracted ocean surface current is more coincident with the in-situ data. We present here the results of estimated ocean surface current and observed in-situ data, which are in agreement within the limit of error bounds

Propagation of near-inertial (18 hours) internal waves were observed on the western part of East (Japan) Sea during May 18 and 19, 2004 from C-band Synthetic Aperture Radar (SAR) image data. Current and temperature measurements carried out during the field experiment from 10 May to 9 June, 2004 (IWXES2004, Internal Wave Experiment in the East Sea), were analyzed to study why near-inertial internal wave can be seen in SAR image and how it evolutes. The spatial distributions of horizontal wavelength and the phase speed estimated from the successively acquired two SAR images, are consistent with those inferred from water temperature and current measurements obtained during the field experiment. Based on these two independent observations (SAR and IWXES2004) we report that the observed wave patterns in the SAR images during IWXES2004 are near-inertial internal waves propagating into the east coast of Korea with phase speed of about 0.3 m/s.

The detailed knowledge of ocean surface wind field is useful for understanding of ocean circulation and variability in a specific spatial and temporal window. An efficient way to obtain such information in sufficient density is through satellite remote sensing. The most recent environmental observation satellite, ENVISAT, has an Advanced Synthetic Aperture Radar (ASAR), which operates at C-band frequency. The ASAR has the potential of retrieving sea surface wind field more accurately than the ERS-1/2 AMI because the ASAR can collect high resolution images with a various viewing geometry and simultaneously in two polarization mode. Several ASAR images including alternating polarization mode and wide swath mode were obtained off the eastern coast of Korean peninsula during the winter season. Simultaneously, we installed two corner reflectors on the near beach to calibrate ENVISAR ASAR data. In this paper we combined polarization ratio models and modified C-band VV-polarization wind speed retrieval models (CMOD/spl I.bar/4 and CMOD/spl I.bar/IFR2) to apply them to ASAR alternating polarization mode data. We compared the resulting wind fields estimated from ENVISAT ASAR data with wind speed and direction measured by the several coastal automatic weather systems and two ocean buoys which are deployed at 10 km and 100 km off the east coast of Korean peninsula respectively.

Along-track interferometric synthetic aperture radar (ATI-SAR) is an active coherent imaging system, utilizing two antennas separated along the platform flight direction. The phase information of ATI-SAR from the Doppler shift of the backscattered signal represents the line-of-sight velocity of the water scatterers. While the advent of ATI-SAR provided us with a potentially powerful technique for ocean surface current and wave mapping, the surface current has not been measured exactly from the ATI-SAR velocity because the Doppler shift is not simply proportional to the component of the mean surface current. It also includes other types of contributions associated with the phase velocity of the Bragg waves and orbital motions of all ocean waves that are longer than Bragg waves. In this paper, we review how the phase difference measured by ATI-SAR is related to the mean Doppler frequency, and we develop a new and practically useful method to extract the surface current component utilizing simultaneously measured L- and C-band ATI-SAR data. Since the measured ATI-SAR velocity shows a different value at a different radar-frequency, we investigate the influence of Bragg-resonant waves and long ocean wave motions on the ATI-SAR velocity according to the radar frequency. The Bragg-wave phase velocity component, which is a significant source of error for extracting the surface current, can be effectively eliminated by using L- and C-band ATI-SAR. The method is applied to L- and C-band ATI-SAR measurements acquired at the Ulsan coast in the southeastern part of the Korean peninsula. The resulting ocean surface current vectors are compared with in situ measurements collected by recording current meter. We furthermore extract ocean surface wave information from the ATI-SAR phase image using a quasi-linear transform.

The along-track interferometry (ATI) SAR measures the Doppler shift of the backscattered signal and thus the line-of-sight velocity of the scatterers. This interferometric velocity is the sum of the orbital motion of water particles from the swell, phase velocities of the Bragg waves, and ocean surface currents. While the advent of ATI SAR provided us with a potentially powerful technique for ocean current mapping, the surface currents cannot yet measured exactly from interferometric velocity measurements. In this paper, we will apply a new method of extracting the surface current velocity from multiple-frequency (L- & C-band) ATI SAR data. We have tested ATI SAR data that were collected during the PACRIM-II AIRSAR experiment over the Ulsan coast on the southeastern part of the Korean peninsula. We have investigated the ocean waves and current features and have retrieved dominant ocean wave information. Furthermore, we could differentiate the ocean current and the Bragg wave phase velocities using multiple-frequency (C & L-band) ATI data.