Polarimetric target decomposition is the most commonly used method of extracting information from polarimetric synthetic aperture radar (SAR) images. Coherent target decomposition methods are usually suitable for high-resolution images. Recently, Paladini reviewed coherent target decomposition methods and proposed a new approach, lossless and sufficient target decomposition (LSTD), using the special unitary matrix SU(4). However, this method suffers from parameter dependence and commutation problems that could introduce errors in parameter estimation such as an erroneous odd-even bounce ratio. In order to overcome these problems, a new model to decompose the circular polarization scattering vector is proposed. In this paper, the model applies a mapping from SU(4) to SU(6) to simplify the target representation while meaningful parameters, which are independent, can be extracted. Fully polarimetric L-band UAVSAR data are used to validate the proposed method. The most important odd-even bounce ratio parameter is used to compare the estimation accuracy between the proposed method and LSTD. Results show that the proposed method can extract parameters more accurately.

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.

One basic issue of importance in polarimetric synthetic aperture radar (SAR) imagery is the identification and separation of target scattering mechanisms. Physical scattering behaviors can be characterized by polarimetric parameters from the second-order statistical observables. The average copolarization phase difference, amplitude ratio, and target coherence are important fundamental parameters for identifying scattering mechanisms. However, the individual usages of these parameters could not describe both the scattering mechanisms and the depolarization. In this paper, a new approach is proposed for scattering characterization by exploring the information contained in these three parameters. First, by assuming reflection symmetry, a new parameter is proposed for the first time to measure the scattering randomness. Then, in combination with the scattering ratio (defined by the ratio of T22+ T33to T11), a classification plane is proposed to classify target scattering mechanisms. A validation test for this new approach is performed with three RADARSAT-2 polarimetric data sets acquired over two study areas: the San Francisco Bay area and Fuzhou, China. Results show that the new approach is very promising for distinguishing orientated targets (with respect to the radar azimuth direction) in urban areas from natural scatterers such as forests, and it also shows that the new method is robust for analyzing multitemporal polarimetric SAR data.

Scattering mechanism identification is an important issue in polarimetric SAR imagery. In this study, we propose a new method for target scattering mechanism classification for the first time. Based on several typical scattering models, including the X-Bragg scattering model, Fresnel reflection model, and volume scattering models, a new polarimetric scattering classification diagram is proposed. It consists of two parameters, i.e., the Bragg alpha angle and a new parameter which is proposed for the first time and can be used to represent backscattering randomness. Results are demonstrated and validated on RADARSAT-2 data of the San Francisco area to illustrate its effectiveness.

A CSR-Based (Contextual Sparse Representation) classification method for PolSAR image is proposed based on the idea of sparse representation and spatial correlation, which incorporates the intrinsic polarimetric information and the spatial contextual information in the sparse representation procedure. Firstly, multiple useful features are extracted to describe PolSAR images at various aspects. Then, the feature vectors of training samples construct an over-complete dictionary. Then sparsely represent the training samples using the over-complete dictionary and obtain the corresponding coefficients. In this step, the spatial neighboring feature-vectors are assumed to have a similar sparse representation way. Specifically, they can be linearly represented by the same atoms while the weights are different. That is the kernel of CSR. In this way, the efficiency of sparse classification can be highly raised and the result can also be improved by adding the contextual information. The proposed method is validated by the Danish EMISAR L-band fully polarimetric SAR data and the experimental results confirm the performance of the proposed method in PolSAR image classification.

With multiple channels, Polarimetric SAR (PolSAR) contains abundant target information and anti-jamming ability, which can improve the ability of target discrimination and image interpretation. The classification problem of PolSAR has become one of the most urgent problems to be solved in PolSAR application with the improvement of PolSAR technology. Due to the complexity of multiple-dimensional classification, single classifier often considers one issue and ignores other aspects, which result in great deviation from the real situation. Integration of multiple classifiers can overcome the above problem; however it is not mean the more numbers of classifiers, the better the result. Therefore, this paper introduces a PolSAR image classification method of selective ensemble learning based on genetic algorithm, which can select several classifiers with better performance from the multiple classifiers to get the excellent result.

This study focuses on the development and evaluation of the generalized optimization of polarimetric contrast enhancement (GOPCE) model to discriminate between forested and nonforested areas. The main objective is to investigate the performance of the GOPCE method for forest mapping and to assess the potential of different polarimetric parameters for forest representation. We make two modifications to the original GOPCE method. First, by comparing behaviors of different polarimetric parameters, the GOPCE model is modified. Then, linear discriminant analysis is employed for further optimization of the target contrast. Forest/nonforest discrimination results are demonstrated on L-band fully polarimetric ALOS-1/PALSAR data acquired over a pilot study area in northeastern Tasmania, Australia, where the main forest type is eucalypt forests. Two other forest classification approaches (i.e., support vector machine and canonical variate analysis) are also tested for comparison. The final results obtained from the modified GOPCE model with the generalized Fisher criterion can improve the forest/nonforest discrimination accuracy.

In this paper, a supervised PolSAR images classification method based on sparse representation is proposed. Firstly, Polarimetric decomposition based on Multiple-component Scattering Model, Cloude-Pottier decomposition and Gray-level Co-occurrence Matrix are implemented to obtain features which can describe PolSAR images at multiple aspects. Then, the training samples are represented in an overcomplete dictionary in which the basic elements are the feature vectors of the training samples and this can be computed by minimize l1-norm. Through comparing the residuals of the reconstructed training samples corresponding to different classes with the original training sample, the optimization problem can be solved and obtain the classification result. The proposed method is validated by the Danish EMISAR L-band fully polarimetric SAR data of Foulum Area (DK) and the preliminary experimental results confirm the performance and potential of the proposed method in PolSAR image interpretation.

Polarimetric Synthetic Aperture Rader (PolSAR) image classification is an important topic of remote sensing image interpretation and application. PolSAR image classification is actually a high dimensional nonlinear mapping problem. Through the use of multiple learning to solve the same problem, ensemble learning can obtain stronger generalization ability than individual classifier. Therefore, in this paper, a PolSAR image classification method based on ensemble learning is proposed, in which the individual pattern classifiers are combined based on Bagging and Boosting ensemble learning to reach an stronger generalization ability and better classification. The verification tests are conducted using EMISAR L-band fully polarimetric data to validate the utility and potential of the proposed method in PolSAR image classification.

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.

The articles in special issue focus on advancements in very high resolution remote sensing technologies and applications.

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.

Advanced information processing and architectures will be needed to bridge the gap between the potential offered by the new generations of sensors and the needs of the end-users to actually face tomorrow's challenges in many applications with a very high societal impact. As remote sensing researchers and engineers, this is our passion, our charge, and our responsibility.

This study aims to understand the effects of the topographic slope variation on the vegetation backscattering characteristics of polarimetric imaging radar. Most of the previous studies in modeling microwave backscattering signatures of vegetated area have been carried out over relatively flat areas. In order to model vegetation scattering mechanisms of mountainous forests, this paper presents an improvement of the radiative transfer model that accounts for the tilted scattering surface beneath a forest canopy. An L-band polarimetric synthetic aperture radar (SAR) data set acquired by the NASA/Jet Propulsion Laboratory Airborne SAR system was used to test and to assess the vegetation scattering model for a sloping forest area. Experimental result shows the proposed model to be adequate for evaluating slope-induced changes in microwave scattering mechanisms.

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.

For SAR signals with different frequency, the scattering characteristic of objects is generally different. Hence, more information can be obtained with multi-frequency SAR. It is easier to detect different targets by using multi-frequency PolSAR data, and even for the man-made targets in forest area. To effectively detect natural and man-made targets, the scattering components of different scattering mechanisms obtained from Land C-band PolSAR data by using polarimetric target decomposition could be combined. The experiment result indicates that different targets can be detected by using multi-frequency PolSAR data.

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.

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.

Tidal flats form a unique ecosystem, playing important roles in the environmental assessment of coastal zones. This study aims to derive surface characteristics of tidal flats from fully polarimetric SAR data sets. The microwave surface scattering models were employed to measure the surface roughness on tidal flats from air-borne and space-borne polarimetric SAR data, such as NASA/JPL AIRSAR and ALOS PALSAR. Surface roughness parameters were estimated from the circular polarization coherence and co-polarized scattering elements. Recent changes in costal wetlands will be continually monitored by using the PALSAR data sets together with other newly developed space-borne SAR systems, such as RADARSAT-2 and TerraSAR-X.

The physical properties of persistence scatterers (PS) are estimated utilizing variety means. Two urban areas of Seoul and Busan in Korea are processed using L-band HH-polarization JERS-1 data. The linear and one-year periodic deformations are monitored. The size and brightness variation of PSs are also estimated. In addition, the concurrence of PSs and the point-like targets are examined and disagreement between the positions of both types of scatterers is identified.

Improvements of polarimetric inversion algorithms is presented in this paper. The confounding influence of roughness and vegetation cover on the estimation of the soil moisture contents is considered in the inversion algorithm that estimates volumetric moisture contents and roughness parameters simultaneously from the pertinent combination of polarization measurements. The estimation of the soil moisture contents from polarimetric SAR data is investigated using L- and P-band AIRSAR polarimetric SAR data collected over the Jeju Island, Korea. Results indicate that the estimation of the soil moisture contents can be expanded to a wider range of terrain types by using both L- and P-band polarimetric SAR data.

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

The eigenvalue-eigenvector-based approach for understanding the scattering mechanisms of polarimetric synthetic aperture radar (POLSAR) data leads to noisy classification results due to arbitrarily fixed zone boundaries in the$H/\overline{\alpha}$plane. In this paper, a new classification scheme that can address the inherent vagueness of class boundaries in the$H/\overline{\alpha}$plane was tested in order to improve the unsupervised classification of the microwave scattering mechanism by introducing concepts related to fuzzy sets. A 2-D fuzzy membership function was developed for the fuzzification of the 2-D$H/ \overline{\alpha}$plane. The proposed fuzzy$H/\overline{ \alpha}$classifier is composed of three steps: fuzzification of the$H/\overline{\alpha}$plane, iterative refinement of membership degrees using the$c$-means algorithm, and defuzzification for the final decision process. The performance of this new approach for the L-band NASA/Jet Propulsion Laboratory's Airborne SAR data obtained during the PACRIM-II experiment was shown to be consistently improved. This new classification technique can be applied to POLSAR data without anya prioriinformation. The fuzzification of the zone boundaries can be further applied to the interpretation of the POLSAR data, e.g., multifrequency classification, retrieval of bio- and geophysical parameters, etc. In order to propose another implementation of the fuzzy boundary representation, we exploited the combination of the$H/\overline{\alpha}$state space and anisotropy information.

Large portion of western coast of Korean peninsular has been reclaimed. Because the reclaimed land stands on weak foundation, the subsidence monitoring is required for civil protection. Newly developed PSInSAR technique is applied to monitor surface deformation on reclaimed land in Incheon Port. There was long lasting subsidence on test area up to 30 mm/year. APS and DEM error are also analyzed for the confirmation of the result.

In this paper an improvement of radiative transfer models that accounts for the depolarizing ground scattering and tilted scattering surface is presented to model vegetation scattering mechanisms. This study is implemented with the real L-band NASA (JPL) AIRSAR data in Jeju volcanic island. The vegetation scattering model in sloping surface is adequate to assess slight changes in microwave scattering mechanisms and to interpret the physical characteristics of eigen-parameters of the coherency matrix in sloping vegetated area.

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.

In this paper, a new classification scheme which can address the inherent vagueness of class boundaries in the entropy- alpha plane is tested for improvements by introducing the fuzzy concept. A two-dimensional fuzzy membership function is developed for fuzzification of the entropy-alpha plane. Proposed classification approach preserves the information on polarimetric scattering mechanisms and overcomes difficulties in dealing with class type mixing along the boundary of different scattering types. I. INTRODUCTION One of the key applications of polarimetric Synthetic Aperture Radar (POLSAR) image is classification of terrain cover types. In 1997, Cloude and Pottier (1) proposed an unsupervised classification algorithm based on another target decomposition theory (2). An eigen-analysis of the coherency matrix provides the polarimetric scattering mechanisms with the matrix characterizing parameters such as the polarimetric entropy H, the polarimetric anisotropy A, and the averaged polarimetric scattering angle α . Using the two-dimensional α

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 potential of using the eigen-analysis of the coherency matrix is discussed for improved understanding of microwave interaction mechanism in vegetated areas. Using a nonsymmetric vegetation scattering model, a theoretical target matrix is generated to compare the eigen-parameters of the coherency matrix with vegetated scattering mechanisms.

The aim of this paper is to demonstrate that the Gaussian Markov random field (GMRF) model can be successfully applied to the classification pf multi-frequency polarimetric SAR data. As a special case of MRF, the GMRF has been shown to be an accurate compact representation from a single-band textured images or multi-band textured images. To apply the method to the classification of inter-channel correlated polarimetric SAR data, we first transformed the data into combination of uncorrelated principal component images. Both intensities (hh, hv, and vv) and phase difference (/spl phi//sub hh - vv/) images of L- and P-band data are considered for classification in the study area in Jeju Island, South Korea. The properties of the transformed data reveal that the images tend to be Gaussian and they are mutually uncorrelated. The GMRF model therefore can be applied to the classification of the transformed polarimetric SAR data. As the GMRF model is a type of classifier based on segment merging, the classification process begins from the initial guess consisting of large amounts of segments. Spatially and statistically similar regions are combined to update the segmented map for each iteration. The final classification map based on polarimetric characteristics shows improvements in the accuracy and efficiency of the classification frame for the tested polarimetric SAR data.

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.

This paper investigated and tested the multi-source data fusion of multi-frequency SAR data with optical sensor, utilizing the mathematical theory of evidence. In this type of data fusion, the correct definition of uncertainty and mass function is very important. Firstly each mono-source data is independently classified using the Bayesian maximum likelihood classification method and the fusion is processed during the decision making stage. For the representation of support and plausibility, mass of evidence is assigned to the candidate labels associated with the frequencies of neighborhood pixels' decision. Then the mass is weighted again by both the distance effect and the global source-specific reliability. In the combination and fusion stage, unions of classes get same mass values to emboss its uncertainty of decision making. Finally Dempster's orthogonal sum method was used to combine the evidence information and the associated level of ignorance, and then the maximum support rule decides the final land-cover types. Classification accuracies before and after each step of fusion processing are compared with reference training data set. We applied this method to the NASA/JPL polarimetric AIRSAR data and KOMPSAT-1 (KOrea Multi-Purpose SATellite-1) EOC (Electro Optical Camera) panchromatic data and obtained noticeably better land-cover classification results.

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.

Data fusion is a very effective technique which can be applied to many remote sensing areas such as classification, monitoring of environmental surveillance and man-made target tracking. In this paper, we tested fusion of multiple frequency (C-, and L-band), multiple polarization (HH, HV and VV) and multiresolution data sets. One can obtain a polarimetric SAR data after enhancing spatial resolution through the image fusion process. In order to fuse multiple SAR data and high spatial resolution data, they have to be geometrically co-registered over the same target area and have the same pixel size (spatial registrations). At this stage, we used the nearest neighbor resampling to avoid spectral distortion by interpolation. Multiresolution polarimetric SAR image fusion was performed using the multiscale image fusion technique-discrete wavelet transform after spatial registrations. To evaluate the spectral fidelity of fused polarimetric SAR data, spectral dissimilarity was calculated at each wavelet decomposition level. The resulting classification map based on polarimetric feature vectors shows better class separation after application of fusion processing than without fusion. The polarimetric SAR data over the Gong-ju areas, tested in this research, were acquired during NASA/JPL AIRSAR PACRIM-II experiment in 2000.

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.

The Hough transform is an efficient numerical method for extracting lineaments or linear features from 2-D geological and geophysical data, including satellite images. Analysis of lineaments has also been widely carried out in geological research, and Landsat TM data have been used most extensively for geological lineament investigations. However, there has been less geological application research carried out in the lineament extraction from synthetic aperture radar (SAR) data. This research has tested several spatial data including Landsat TM, JERS-1 SAR and DEM data for automatic geological lineament analyses. The Hough transform technique is applied here for the extraction of lineaments from preprocessed binary spatial data. The multiresolution method is tested to extract lineaments efficiently, because geological lineaments usually appear in various length scales. The Kyungsang Basin in the southeastern part of the Korean peninsula was selected as the test site in this research. The lineaments are extracted from JERS-1 SAR, Landsat TM, and DEM data using both manual and the automatic Hough transform method. The results of lineament extraction indicate that the predominant direction is the NNE direction of azimuth angle 0/spl deg/-30/spl deg/, and the second dominant directions are the NNW, NE, and WNW directions. The results obtained in this research agree well with the regional trends and also with the local geological maps.

Empirical investigations of classification algorithms for lithological discrimination were performed in this research using two study areas in northern Canada: Melville Island, Nunavut, and the Cape Smith Belt of northern Quebec. These investigations suggest that a consensus neural network classifier (a majority-vote algorithm that combines the classification results of ten feedforward backpropagation neural networks) is capable of consistently producing results that approximate those produced by the best individual neural network execution, and that are superior to those generated by the maximum likelihood and evidential reasoning classifiers. The majority-vote consensus routine serves to eliminate the effect of the natural variability among individual neural network classification results, by producing strong results without necessitating the manual evaluation and ranking of all individual neural network classifications. Image classifications produced using the evidential reasoning classification algorithm were generally very poor when measures of initial evidence were derived from the proportions of training data that contained the image values being classified. Evidential reasoning classifications that derived measures of initial evidence from output activations generated by multiple neural network classifications were typically comparable to those of the neural network consensus algorithm.

Multispectral airborne data from the NASA MODIS/ASTER airborne simulator (MASTER) were acquired in Kochang area, Korea, during the PACRIM II mission in 2000. The objective of this study was to evaluate the effectiveness of MASTER data for mapping land cover classes. The 25 bands, including 11 visible and near infrared (VNIR) bands and 14 shortwave Infrared (SWIR) bands, were selected for land cover mapping in the area. Data was calibrated using the flat field method. Various methods, such as Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), were used for feature extraction to determine the inherent dimensionality of the data and to reduce the computational requirement. Spatial texture was extracted from some components obtained from above transforms. Vegetation Index was also computed from the calibrated data. Selected components, texture, and vegetation Index were combined for land cover mapping. Maximum likelihood was utilized for classification using different data combinations. The study showed that different land cover classes could be discriminated from MASTER data using contemporary data processing strategies. Further work is planned to fuse MASTER data and KOMPSAT EOC data as well as other available data for land cover classification.

Two types of polarimetric Target Decomposition (TD) theories were used to investigate the effects of surface slopes on the physical scattering mechanisms over mountainous scattering surfaces. This study is implemented with the L- & P-band NASA (JPL) airborne SAR (AIRSAR) data, which was acquired during PACRIM-II Korea campaign in September 2000. Although the dominant scattering mechanism of mountainous forest area is volume scattering in pine forest scattering surface, detailed scattering mechanism varies with respect to the local incidence angle variations due to the local topography and wavelength.