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Enhancement of vessel detection performance in synthetic aperture radar (SAR) images generated academic advancements related to amelioration of the algorithmic accuracy and training data procurement. For practical implementation of vessel detection algorithm to maritime surveillance, however, presentation of authentic position of vessels was essential. Accordingly, this article aimed to propose an algorithm, which demonstrated realistic and azimuth shift-corrected position of vessel, especially out of conventional vessel monitoring apparatus: automated identification system (AIS) and vessel-pass (VPASS) information. Two different analyses regarding the vessel detection output utilization were, therefore, presented. Primary analysis demonstrated a vessel identification algorithm, comparing the vessel detection output with elaborately preprocessed AIS and VPASS information, which indicated the discrete position and velocity of vessel. The other presented a position restoration algorithm via i) velocity estimator complementing the conventional fractional Fourier transform velocity estimation analysis, while investigating the effect of range acceleration in deriving the azimuth velocity and ii) measuring the vessel orientation angle from Radon transform. Both algorithms were implemented to the vessel detection output in Cosmo-SkyMed SAR images, depicting an enhanced accuracy compared to the conventional algorithm both in velocity estimation and azimuth shift estimation; velocity offset reduced from 1.64 m/s to 1.29 m/s and average azimuth shift offset reduced from 70.75 m to 62.39 m. The presented algorithms would be decisive in terms of practicality if robustly attached to convolutional neural network-based vessel detection algorithms demonstrating ideal detection performances.

Accurately tracking marine traffic considering security and commercial activities is still challenging despite its increasing global importance. Recently, space-borne synthetic aperture radar (SAR) is being considered to accurately monitor maritime traffic, and techniques to detect the position of ships and estimate their velocity have become essential. Here, we investigated the potential for automatic estimation of ship velocity using the azimuth offset between ships and wakes detected using convolutional neural network (CNN) coupled with SAR imagery. We found that azimuth offset is proportional to the Doppler shift effect of the back-scattered signal in SAR, thus, it relates to the radial velocity of a moving target. Consequently, we propose a method whereby a CNN is applied to automatically detect ship wakes from TanDEM-X data. In this method, ship velocity is calculated using the azimuthal distance (i.e., azimuth offset) between the stern of the detected ship and the vertex of the detected V-shape wake-determined as the intersection of two lines obtained through edge filtering and Radon transforms. The location and number of detected ships are then compared with an automatic identification system (AIS), and the calculated velocity of the ship is compared with the velocity obtained via along-track interferometry and AIS. Results show that our method automatically detects ships and wakes with accuracies of 91.0% and 93.2%, respectively, and estimates the velocity of ships with an accuracy of 0.13 m/s. This method is effective when wind velocities are not substantially higher than 5.5 m/s and ship velocities are not extremely low.

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.

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.

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.

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.

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.

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 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.

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

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.