This paper aims to establish physical interpretation of nine-component scattering power decomposition using all coherency matrix elements/parameters for fully polarimetric SAR data analysis. It has been known that complete scattering mechanisms can be characterized by using all nine parameters of coherency matrix. We try to decompose the coherency matrix data in a physical scattering manner, as previously reported in a geometrical way by Huynen. New physical scattering models of real and imaginary part ofT12are introduced, which represent dipole scattering power and quarter wave plate scattering power, respectively. These models are added to the existing scattering models for ideal case of surface scattering, double-bounce scattering, and volume scattering. A quantitative analysis of the oriented urban patch of Mumbai in the L-band dataset result reveals a 9.7% decrease in volume scattering, which avoids misinterpretation between vegetation and the oriented urban area, and the 22.5% and 13.5% contributions come from dipole and quarter wave scattering powers, respectively. It is confirmed that proposed method produces better results and interpretations when compared to those by the existing decomposition methods.

This letter proposes a novel synthetic aperture radar (SAR) time series analysis method based on the scattering power decomposition algorithm with a reasonable ensemble average in both temporal and spatial domains. We reveal that the ensemble average is effective not only in the spatial domain but also in the temporal–spatial domains in the scattering power decomposition. That is, if we extend the ensemble average window in the temporal domain, the proposed method can accurately achieve volume scattering power with a higher spatial resolution than conventional approaches. The precise volume scattering power serves accurate forest monitoring. As an application, we performed forest disturbance detection in the Amazon rainforest using Sentinel-1 time series data. The proposed method detected the disturbances earlier, in less than 2 months, compared to other methods that take about 3 months.

The 2 × 2 relative scattering matrix [S] is characterized by five elements (three amplitudes and two relative phases). A more suitable representation of the data in terms of power expression for target identification exists in the 3 × 3 Paulibased polarimetric coherency matrix [T]. The aim of this work is to expand the physical interpretation of [T] elements in terms of a physical scattering mechanism, which has not yet been achieved completely. Compound scattering matrix nature is considered to interpret the elements of [T]. Compound scattering phenomenon, and thereby generated matrices, are verified with measured compound scattering matrices in an anechoic chamber by comparing measured and theoretical polarization signature responses. The behavior of measured polarization signature(s) is consistent with the theoretical/hypothetical polarization signature(s). It is also observed that the combination of two or more dipole-type targets formed the scattering generators and helped to reveal the unknown scattering mechanisms in the coherency matrix. Furthermore, the existence of compound scattering phenomenon is also justified with airborne and spaceborne fully polarimetric synthetic aperture radar (POLSAR) data by implementing recent physical model-based scattering power decomposition methods.

TomoSAR is one of the methods which can perform three-dimensional (3-D) imaging with multiple SAR datasets by using Direction-of-Arrival (DOA) estimation techniques to reveal height distribution of scatterers. However, resolution of the TomoSAR is generally insufficient for height estimation of small objects on the ground surface. Also, if we employ a high resolution DOA estimation method for the height estimation, its performance is often degraded due to orbital error. To solve this problem, we employ ESPRIT algorithm and propose a new method named ESPRIT-TomoSAR. The ESPRIT algorithm is one of high resolution DOA estimation techniques using orthogonality between signal and noise subspace, it has not only high resolution performance equivalent to the MUSIC algorithm but also robustness for orbital error. In this paper, to verify effectiveness of the proposed method, we provide experimental results by using actual airborne SAR dataset. The experimental results show that the proposed method realizes high resolution and robustness for orbital error, and it is effective for a precise height estimation such as the structure analysis of buildings.

In this paper, we carry out polarimetric scattering analysis for simple dihedral structure, to realize accurate detection of manmade object damaged by flood-related mudflow, using quad polarimetric SAR data. The Finite Difference Time Domain (FDTD) method is utilized for the analysis. The dihedral model is composed of thin perfectly electric conductor (PEC) plates. Dielectric slab with gentle slope, which simulates wet soil in mudflow, is set on one of the PEC plates. The dependency of the polarimetric scattering on change of condition of the covered dielectric is investigated in detail.

Scattering power decomposition has been a hot topic for two decades for the analysis of fully polarimetric SAR data. It has advantages such that 1) easy to interpret the decomposition image, 2) RGB color-coding represents scattering mechanism directly, 3) easy to implement, 4) fast computation time, 5) decomposition powers can be used for further analyses, classification, etc. This paper reviews the development of scattering power decomposition from the original 3-component to recent 6-component schemes, and present a new 7-component decomposition using San Francisco image acquired with ALOS-2 quad. pol. data.

Applications of fully polarimetric synthetic aperture radar (POLSAR) have increased in the past few decades. The potential of model-based decompositions is coupled with polarimetric information extraction from the POLSAR data for target identification and classification. The coherency matrix [T] with nine independent parameters, and associated with some physical scattering models, serves as input to these decompositions. This paper attempts to assign one such physical scattering model to the real part of T23(Re{T23}) and develop a new scattering power decomposition model, called as the seven component scattering decomposition (7SD). Previously developed scattering power models have eliminated Re{T23}, assuming the orientation angle compensation condition, to reduce the number of independent [TI parameters. The proposed 7SD model has been tested on fully polarimetric SAR data sets acquired by the spaceborne Advanced Land Observing Satellite-2/Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2) and airborne F-SAR, and the results are compared with the existing scattering power decompositions. The physical scattering model for Re{T23} is derived from a particular configuration of dipoles (referred to as “mixed dipole” configuration), which gives rise to compound scattering. The mixed-dipole scattering occurs in urban areas that are highly oriented to the radar illumination direction as well as in vegetation areas. 7SD also delivers an additional mixed dipole scattering power compared to the previous six-component scattering model. The mixed-dipole scattering model reduces the contribution of volume scattering power in double-bounce predominant areas (such as oriented urban blocks), thereby imparting improved understanding of the polarimetric information contained in the coherency matrix.

In this paper, we discuss the application of polarimetric target decomposition (TD) theorems to circular synthetic aperture radar (CSAR) images to retrieve the polarimetric azimuthal angular characteristics of the targets. In CSAR systems, radar-carrying aircraft moves along a circular path. The antenna beam is pointed toward the center of the circular path during the data acquisition to irradiate the spotlighted area from various azimuthal or aspect angles. Therefore, the resultant polarimetric CSAR images contain information about the polarimetric scattering mechanisms of the targets at each aspect angles. To recover this information, we propose the use of spectral decomposition of CSAR images in combination with several TD algorithms. The spectral domain CSAR images can be viewed as the azimuthal angular spectrum images. Thus, the application of TD algorithms to the spectral domain images is expected to be able to obtain the angular dependence and polarimetric scattering properties of the targets simultaneously. We carried out a simple numerical simulation and an indoor laboratory experiment to validate the proposed TD scheme.

Disaster area monitoring is one of the most important applications in PolSAR data utilization. In this paper, we propose a novel usage of polarimetric orientation angle shift to grasp state of bridge under severe disaster situation due to a heavy rain or an earthquake. We carry out polarimetric scattering measurement for a simple scaled bridge model (girder bridge model) with/without damage, to acquire quad-polarimetric SAR data. It is verified from the result of the PolSAR image analysis for the measured data that the polarimetric angle shift is a useful indicator for distinguishing the healthy and damaged bridge models.

This paper presents polarimetric response from conifer and broad-leaf tree at Ku-band in anechoic chamber. A fully polarimetric measurement was conducted under precisely controlled environment to examine the difference of scattering nature from trees at 13-17 GHz. Polarimetric responses using the 4-component scattering power decomposition and alpha bar angle by eigenvalue analysis yielded clear distinction between these tree types. It was confirmed that fully polarimetric information with high-resolution data provides good separation of these trees.

Fully polarimetric model-based decompositions are developed by accounting for the physical scattering model and experimental polarimetric SAR data acquisition processes. These decompositions offer the promising straightforward interpretation and highly improved inversion models for visualizing images of scattering scenarios optimally. However, the attempts in existing decompositions to implement the split real and imaginary components of the$T_{13}$element of the coherency matrix have been hampered by the absence of physical models to fit the coherency matrix. In this paper, two additional physical scattering submodels are derived. The real and imaginary parts of$T_{13}$are accounted for by implementing two newly developed physical scattering models. (One is for oriented dipole scattering and the other is for oriented quarter-wave reflection.) Furthermore, this paper is extended by implementing these physical models into a six-component scattering power model-based decomposition. To this date, the developed novel decompositions account for the maximum elements of the coherency matrix in a physical manner compared to the existing model-based decompositions. The proposed novel decomposition is tested on L-band and X-band fully polarimetric SAR data sets of the Advanced Land Observing Satellite-2/Phased Array L-band Synthetic Aperture Radar-2 and the X-band TerraSAR-X, respectively. This new decomposition produces additional two scattering submatrix components. Such scattering components are prevalent in vegetation and urban areas and even dominant over highly oriented urban scenarios. The new method enhances the truly existing double-bounce scattering contributions and reduces the overrated volume scattering from double-bounce scatterers. By comparing the results, it is found that the proposed decomposition considerably enhances the SAR image quality and its more correct visualizing presentation compared to existing decompositions. It is also found to be more robust over the oriented urban areas than the existing decompositions, resulting from the utilization of both the real and imaginary components of$T_{13}$polarimetric information in a physical scattering manner.

We presented two target decomposition methods for polarimetric interferometric SAR (PolInSAR) images parallel to the eigen-decomposition and scattering power decomposition for polarimetric SAR (PolSAR) images. For both cases, we showed the importance of PolInSAR coherence estimation, and in particular, the newly proposed best normal matrix approximation algorithm in successful decomposition of PolInSAR data. Preliminary results were demonstrated using the ESAR data.

Forest disaster monitoring requires frequent data observation of remotely sensed data. It is getting more common to use open satellite image archives to monitor land use change from multi-temporal data. In this study, multi-temporal DSM created by UAV-SfM is used to identify forest gap. ALOS 2 full polarization data is compared with the finer scale height difference detected by multi-temporal UAV based DSMs. Advantage of UAV based observation is to identify forest gap area efficiently in wide area and collect more samples to compare wide coverage of the satellite radar backscatter to understand backscatter characteristics. As a result, HH and HV polarization has detection power of forest gap size.

This paper proposes a model-based and 6-component scattering power decomposition method, which accounts for all 9 independent polarimetric parameters in the coherency matrix formulation. Both the real and imaginary parts of T13 component are accounted for using +/-45 degree oriented dipoles and newly introduced compound scattering matrices. The compound scattering matrix can be realized by combination of oriented dipoles. By using 6-components and rotation of coherency matrix around the radar line of sight, all coherency elements are accounted for the decomposition. The volume scattering power is reduced compared to those of the existing decomposition methods. The proposed decomposition becomes suitable, especially for vegetation area analysis in more detail.

SAR Tomography, or TomoSAR, is one of the powerful SAR data analysis tool with multi-baseline data set, especially for height estimation of scattering centers. On the other hand, PolSAR, or Polarimetric SAR, is also powerful tool for scattering characteristic discrimination of terrain. Several PolSAR analyses have applied to the TomoSAR. However the application of model-based PolSAR scattering power decomposition techniques have not reported yet. The model-based technique has superior performance to discriminate scattering property of local scatterers, hence it will assist to enhance resolution performance of TomoSAR with a limited multi-baseline dataset. In this paper, we show a basic concept of the model-based PolSAR scattering component decomposition for TomoSAR, and demonstrate it by using the multi-baseline dataset obtained by the PiSAR2-X.

This paper focuses on the development of a novel algorithm for deriving snowpack density over the snow-covered region of the Himalayas. The analysis utilizes fully polarimetric TerraSAR-X synthetic aperture radar data sets, field observations, and other ancillary information for the retrieval of snowpack density. The algorithm involves the development of a new generalized hybrid decomposition model. The generalized volume scattering parameter from the decomposition model is inverted for snow density estimation. A few field data measurements' campaigns were carried out, within near-real time of satellite passing over the area, to collect various parameters such as temperature, water content, and the density of the snowpack at varying depths. These field observations are further used for validation of the results obtained from the inversion algorithm. It is also found that the model-estimated snowpack density is highly congruent with the field-measured snowpack density. The mean absolute error of snowpack density, root-mean-square error, and index of agreement are found to be 9.9 kg/m3, 10 kg/m3, and 0.96, respectively, which are well within the acceptable range.

The properties of ionosphere govern the way in which radar signals traverse, predominantly at the L-band and lower frequencies. The interaction of ions with the traversing radar signal cause rotation of polarization vector, commonly called as Faraday rotation (FR). For a well calibrated monostatic radar, the backscatter measurement reciprocity is disrupted due to FR and other system parameters, like system noise. With the knowledge of full polarimetric ALOS-2/PALSAR-2 data available, FR estimation and compensation is described in this study over Mumbai, India. T4coherency matrix based estimated Faraday rotation angle is applied to correct PALSAR data. The work is extended to calculate total electron content (TEC) and compare the Y4R and G4U scattering power decompositions. The mean two way FR values of 8.47° was observed with the mean TEC over Mumbai calculated to be 7.069 TECU. Scattering powers results show a significant increase in the double bounce powers over the region.

This paper proposes a simple polarimetric angle compensation to make accurate detection of deformed/inclined buildings possible by using quad-pol SAR data. It is expected that by applying the new compensation to the coherency matrix after doing conventional orientation angle compensation (OAC), undesired large surface scattering contribution generated between the inclined sidewall of the building and ground plane may be reduced. Cooperating with the pro-posed compensation and the angles α̅, β̅ obtained by the eigenvalue/eigenvector analysis, we can construct simple and efficient man-made objects detection (MOD) algorithm appropriate for identification of the deformed/inclined buildings.

Scattering component decomposition techniques for POLSAR data are powerful tool for the POLSAR data analysis. The three component decomposition technique is the one of the major technique among them. However, the algorithm requires several assumptions to solve the problem because of the lack of independent observables. To overcome this difficulty, we have proposed the multi-baseline POLSAR scattering component decomposition technique which utilizes the concept of the MUSIC algorithm with spatial smoothing preprocessing (SSP) scheme. However resolution performance depends on the decorrelation performance by the SSP. In this report, we propose several enhanced schemes for the SSP, and show some experimental results to show their performance.

Extraction of polarimetric information from SAR data is one of the most important issues in SAR applications. Since fully polarimetric SAR data and model-based scattering power decomposition are now available, its real utilization becomes important topic. In this presentation, some data sets from PiSAR-L2, ALOS, ALOS-2 systems are shown using the existing model-based scattering power decompositions. The observation areas are chosen so that each scattering mechanism correspond specific scattering power utilization, i.e., landslide area for the surface scattering, urban expansion for the double bounce scattering, deforestation for the volume scattering. The final color-coded images provide us with direct way to understand the scattering scenario in the real world.

This paper presents model-based scattering power decomposition images acquired with Advanced Land Observing Satellite 2 (ALOS-2) operating at the L-band frequency. Using the advantages of penetrating and fully polarimetric capabilities in the L-band, real applications on the environmental monitoring such as forest change, flooding, volcano investigation, etc, became feasible. Some evident images are shown in this paper using the existing model-based scattering power decomposition.

Field tree measurement to collect ground truth data is labor intensive. To make it efficient to collect the field data in a large scale, Unmanned Aerial Vehicle is introduced in this study. An automated photogrammetric technique called Structure from Motion (SfM) is used to create 3D data out of photos collected by UAV. Tree structure created by SfM is compared with the 3D data obtained by several Terrestrial Laser Scanners to see the difference in vertical forest structure. Canopy structure is mainly captured by UAV-SfM and there is more point density around tree canopy than 3D data obtained by airborne laser. The point density of UAV around tree canopy is equivalent to that of TLS data, but tree trunk underneath the canopy cannot be reconstructed well, because the trunk is obscured by tree canopy and it is not visible on photos.

This paper proposes a simple algorithm for detecting largely-oriented man-made object by using angular index β, in addition to the conventional angular index a. The angular indices are derived by the eigenvalues/eigenvectors analysis to the rotated coherency matrix of fully polarimetric SAR data. Here the orientation angle compensation (OAC) is carried out to the coherency matrix before doing the eigenvalues/eigenvectors analysis. In the proposed algorithm, thresholds Th1, Th2for α, β are included and must be determined for accurate detection of the objects. In order to determine them, we carry out the Finite-Difference Time-Domain (FDTD) polarimetric scattering analysis for relatively large scale man-made objects models. The dependency of the polarimetric scattering feature on the variation of squint angle is in detail investigated. Resultantly, it is found from the FDTD analysis that the candidates of appropriate thresholds for α and β are obtained for largely-oriented man-made objects detection.

An adaptive general four-component scattering power decomposition method (AG4U) is proposed in this letter. The degree of polarization mis used as a criterion for the adaptive nature of the proposed decomposition. In this method, one among the two complex special unitary transformation matrices is chosen to transform a real unitary rotated coherency matrix based on the largest value of m. This transformed matrix is then utilized for the existing Yamaguchi et al. four-component decomposition scheme with an extended volume scattering model. The proposed decomposition is applied to Radarsat-2 full-polarimetic C-band data over San Francisco and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) full-polarimetric L-band data over the Hayward Fault in California. The scattering powers estimated from the decomposition techniques of Yamaguchi et al. (Y4O), Singh et al. (G4U), and AG4U are compared. AG4U shows appreciable improvements in the scattering powers, particularly in urban areas oriented about the radar line of sight compared with the Y4O and G4U decompositions. It also shows reduced percentage of pixels with negative powers considerably compared with the Y4O decomposition.

In this paper, we address the theoretical aspects of coherence region modeling and its inversion methods for polarimetric interferometric synthetic aperture radar (PolInSAR). Instead of only considering the geometrical shape of the coherence region, we focus on directly modeling the whitened interferometric cross-correlation matrix of the PolInSAR data. In particular, we consider three classes of generic scattering models that respectively contain single, double, and triple phase centers within one resolution cell. We then demonstrate, for each class, that the modeled whitened interferometric cross-correlation matrix is normal. Based on this property, we propose, for each case, efficient algorithms to obtain the best normal matrix approximation solution for model inversion from the observed whitened interferometric cross-correlation matrix. In addition, we show how a simple and effective criterion can be designed from the derived solutions for model validation. Finally, we verify the solutions with both simulated and real data. The results prove the proposed method to be a very promising tool for PolInSAR applications.

Various remote sensing technologies are utilized to monitor tropical forest for REDD project. In developing countries, the accuracy of field measurement does not satisfy the quality required for the validation of satellite remotely sensed data. Therefore, we introduce the most portable laser sensor, SICK LMS511, to measure trees in the field of tropical forest and the semi-automatic process to derive tree parameters from the 3D point cloud is established to provide accurate forest inventory data. From the accuracy assessment of the portable laser system provided 3.61 cm of root mean square error (RMSE) of stem diameter and 0.50 m of RMSE of tree height measurement. The portable terrestrial laser sensor can be the standard technique to create forest inventory data for the field validation of satellite remote sensing.

A heavy rainfall was observed by the NICT X-band airborne synthetic aperture radar (SAR), Pi-SAR2. In the Pi-SAR2 image there are features attributed to both backscattering from the rainfall and attenuation by the rainfall, as published for the TerraSAR-X (spaceborne X-band SAR) obaservation. Those features induced by the rainfall are evaluated with the Pi-SAR2 polarimetric image data. It is also taken into account how movement of the raindrops affects its SAR imagery.

There is currently a great deal of interest in the use of radar polarimetry for disaster monitoring. In this context, this paper presents a result of numerical assessment of landslide detection methodology based upon the change of scattering power between multi-temporal fully polarimetric synthetic aperture radar (POLSAR) data. There are several types of natural disaster. Among them, we tried to detect sediment disaster including landslide, slope failure, debris flow, from POLSAR data. Sediment disasters are those that occur on mountain slopes. Mountain slopes in warm and humid regions such as Japan are usually covered by vegetation. Sediment disasters are triggered by heavy rainfall or an earthquake. Soil and vegetation flow down the slopes. Therefore the original slope changes to a slope with bare soil. The scattering process of the microwave comes to the surface scattering from the volume scattering. Therefore, we select a mountainous area stricken by a large earthquake. The four-component scattering model decomposition was applied to the POLSAR data over the area. The data acquisitions were conducted before and after the earthquake. Compared sediment disasters interpreted from aerial photographs with the scattering power decomposition images; it is revealed that the surface scattering increased after the event as compared to the volume scattering as expected.

This paper presents very high resolution (30 cm) and fully polarimetric images acquired with a new airborne Polarimetric Interferometric Synthetic Aperture Radar 2 (Pi-SAR2) system operating at the X-band. Pi-SAR2 is a successive version of the former X-band Pi-SAR aiming at exploring the possibility of fully polarimetric SAR. In collaborative works among NICT and research institutions in Japan, data acquisition flights over Niigata have been carried out on August 25 and November 17, 2013. Some imaging results with interesting phenomena are shown using polarimetric scattering power decomposition.

In this paper, a general scheme for complete model-based decomposition of the polarimetric synthetic aperture radar (POLSAR) coherency matrix data is presented. We show that the POLSAR coherency matrix can be completely decomposed into three components contributed by volume scattering and two single scatterers (characterized by rank-1 matrices). Under this scheme, solving for the volume scattering power amounts to a generalized eigendecomposition problem, and the nonnegative power constraint uniquely determines the minimum eigenvalue as the volume scattering power. Furthermore, in order to discriminate the remaining components, we propose two approaches. One is based on eigendecomposition, and the other is based on model fitting, both of which are shown to properly resolve the surface and double-bounce scattering ambiguity. As a result, this paper in particular contributes to two pending needs for model-based POLSAR decomposition. First, it overcomes negative power problems, i.e., all the decomposed powers are strictly guaranteed to be nonnegative; and second, the three-component decomposition exactly accounts for every element of the observed coherency matrix, leading to a complete utilization of the fully polarimetric information.

This study discusses the capability assessment of fully polarimetric L-band spaceborne synthetic aperture radar (SAR) for detection of seasonal snow covered areas. In this paper, ALOS PALSAR time-series data sets obtained in quad-pol modes have been investigated to evaluate the polarimetric signal scattered from a snow-covered mountainous ecosystem in Niigata, Japan. Results show that changes in the scattering mechanism across the various snow states can be identified from polarimetric parameters. In particular, different polarimetric parameters offer complementary information on the snow properties. Based on the characteristic seasonal changes of polarimetric parameters, a new method to map snow-covered areas is proposed in this study using an information fusion approach. Snow extent can be identified successfully by combining polarimetric indices with an overall accuracy of 74.4% as compared with in situ measurements and 77.0% as compared with optical images.

This paper examines the capability assessment of fully polarimetric L-band data for the snow and nonsnow-area classifications. The data sets used are the fully polarimetric Advanced Land Observation Satellite–Phased Array-Type L-Band Synthetic Aperture Radar data, optical Advanced Land Observing Satellite (ALOS)-advanced visible and near-infrared radiometer-2 data close to the radar acquisition, and environmental satellite–advanced synthetic aperture radar data. Several parameters are used to discriminate the snow-covered areas from nonsnow-covered areas in the Indian Himalayan region, including backscattering coefficients, the ratio of cross/copolarized backscattering power and polarization fraction (PF) value. Supervised classification schemes are employed using polarimetric decomposition methods based on the complex Wishart classifier. The accuracy of the classification was found to be 97.95% for the Wishart-supervised classification. Among various parameters and methods, it was found that the alternative newly proposed PF scheme, based on the implementation of fully polarimetric synthetic aperture radar data, yielded the best classification result in the absence of the training samples. The PF value has been effective for discrimination of the snow-covered areas from nonsnow-covered areas, debris-covered glacier, and vegetation. The results of this investigation show that L-band fully polarimetric SAR data provide considerable improvement but may not possess the optimal capability to discriminate snow from other inherent natural and man-made scatterers in heavy snow-laden mountainous scenarios, which may require fully polarimetric S-band or C-band PolSAR measurements.

In this study, the usability of the co-polarization correlation coefficients of polarimetric SAR data for characterization of terrain types in the natural and urban areas has been investigated. The co-polarization coherence signature plot and the co-polarization phase correlation plot, which display the correlation coefficients as a function of the polarization state, are introduced with the unitary basis transformation of the polarimetric covariance matrix. Experimental results indicate that the co-polarization coherence signatures can be an effective way to characterize terrain scatter types.

This paper investigates polarimetric scattering characteristics for accurately detecting stricken man-made objects. In order to understand the polarimetric scattering features generated from various damaged man-made buildings, we carry out the Finite-Difference Time-Domain (FDTD) polarimetric scattering analysis for deformed and/or collapsed man-made target models. The dependency of the polarimetric scattering feature on the variation of squint angle is in detail investigated. It is confirmed from the estimation based on various polarimetric analyses that the double-bounce scattering, the co-pol correlation coefficient in LR circular porlarization basis, and its normalized one are useful markers for stricken man-made objects detection/identification.

It is known that all weather capability makes radar remote sensing the most suitable measure for natural disaster monitoring. Among them, fully polarimetric SAR (POLSAR) data with amplitude and phase information, i.e., the scattering matrix; is possible to retrieve the object information in more detail regardless of the weather. We applied polarimetric method to POLSAR data over actual disaster suffered area to show the efficiency of POLSAR data for disaster monitoring. A large scale landslide was clearly detected in a single decomposed image. Also, identifying landslides newly generated by an earthquake was possible to compare decomposed images of pre and post an earthquake. It is important that utilizing images observed in different direction (orbit) to reduce omission errors.

In this letter, an advanced version of the hybrid Freeman/eigenvalue decomposition technique for land parameter extraction is presented with an illustrative example of application. The motivation arises from decomposition problems in obtaining a meaningful volume scattering estimation, so that the technique can be used for both oriented objects and vegetation/forest areas. The idea is to improve the accuracy of the required parameter extraction. Two strategies are adopted to increase the applicability of a hybrid Freeman/eigenvalue decomposition technique: One is the unitary transformation of the coherency matrix; the other is to use an extended volume scattering model. The extension of the volume scattering model plays an essential role for the hybrid Freeman/eigenvalue decomposition technique. Since the volume scattering power is evaluated by assuming that the$HV$component is caused by vegetation only in the existing technique, an extended volume scattering power approach is utilized. It is shown that vegetation areas and oriented objects such as urban building areas are well discriminated by the proposed technique as compared to the existing techniques.

This paper reflects the polarimetric synthetic aperture radar (POLSAR) data utilization for near-real-time earthquake and/or tsunami damage assessment in urban areas. In order to show the potential of the fully polarimetric high-resolution polarimetric SAR (POLSAR) image data sets, a four-component scattering power decomposition scheme has been developed and applied to monitor near-real-time earthquake and tsunami disaster damages. The test site for natural disaster damages has been selected: parts of the coastal area affected by the March 11, 2011, 9.0 magnitude earthquake that struck off Japan's northeastern coast and triggered a super-tsunami. The color-coded images of the scattering power decomposition scheme are a simple and straightforward tool to interpret the changes over the earthquake/tsunami affected urban areas and man-made infrastructures. This method also holds other types of natural (typhoon or tornado) and man-made disaster assessment applications. It is found that the double-bounce scattering power is the most promising of the input parameters to detect automated disaster affected urban areas at pixel level. It is also observed that the very-high-resolution POLSAR images are required for superior urban area monitoring over the oriented urban blocks with respect to the illumination of radar.

The statistical behavior of the sea clutter in synthetic aperture radar (SAR) images is characterized by both the marginal distribution and the spatial correlation. However, simultaneous modeling of the joint information remains a difficult job because of the non-Gaussian clutter nature. In this paper, a semiparametric approach is proposed for addressing this problem. First, we investigate the applicability of the nonparametric kernel density estimator (KDE) for estimating the marginal distribution of the SAR clutter and show that the KDE is most applicable in the log-intensity domain. Second, we propose to estimate the underlying spatial correlation structure with a copula approach and show that the Gaussian copula is a sufficiently accurate model. Consequently, the KDE, together with the Gaussian copula, offers a full characterization of the joint probability distribution, based on which a quadratic detector of null distribution governed by the well-known chi-squared law can be conveniently designed for constant false alarm rate detection. In the experiment, results with both simulated and real SAR data demonstrate that, compared with the single-point detector using only the marginal distribution, the proposed method, which incorporates spatial correlation, significantly improves the detection performance with regard to either the receiver-operating-characteristic curve or detected target pixels. The tradeoff, however, lies in a loss of false alarm rate control resulting from increased uncertainty in estimating higher dimensional distributions.

This paper presents a new general four-component scattering power decomposition method by implementing a set of unitary transformations for the polarimetric coherency matrix. There exist nine real independent observation parameters in the 3$\times$3 coherency matrix with respect to the second-order statistics of polarimetric information. The proposed method accounts for all observation parameters in the new scheme. It is known that the existing four-component decomposition method reduces the number of observation parameters from nine to eight by rotation of the coherency matrix and that it accounts for six parameters out of eight, leaving two parameters (i.e., the real and imaginary parts of$T_{13}$component) unaccounted for. By additional special unitary transformation to this rotated coherency matrix, it became possible to reduce the number of independent parameters from eight to seven. After the unitary transformation, the new four-component decomposition is carried out that accounts for all parameters in the coherency matrix, including the remaining$T_{13}$component. Therefore, the proposed method makes use of full utilization of polarimetric information in the decomposition. The decomposition also employs an extended volume scattering model, which discriminates volume scattering between dipole and dihedral scattering structures caused by the cross-polarized$HV$component. It is found that the new method enhances the double-bounce scattering contributions over the urban areas compared with those of the existing four-component decomposition, resulting from the full utilization of polarimetric information, which requires highly improved acquisitions of the cross-polarized$HV$component above the noise floor.

This paper introduces a novel method for filtering polarimetric synthetic aperture radar (POLSAR) imagery data. Unlike most existing filters, the proposed one is freed from using searching windows but implements the filtering in a sequential fashion. Specifically, we propose to use the inverse Wishart distribution as the conjugate prior model for the coherency matrix (CM) data and show that the maximum a posteriori (MAP) solution leads to a very simple linear recursive formula for estimate update, through which issues such as feature preservation and bias correction can be also taken into account. In the experiment, the proposed method is tested on the POLSAR dataset acquired by the DLR (Germany Aerospace Center) F-SAR system. Results demonstrate that the filter shows inspiring performances in terms of both noise reduction and feature preservation.

High resolution active remote sensing data are getting more accessible. ALOS PALSAR radar data can provide 10 to 30 m resolution data. With those resolutions, land use changes can be detected and monitored well. The land use map of multi-temporal data is made by radar images, but seasonal change caused by deciduousness should be separately identified from the human activities. In this study, airborne lidar is used as ground truth data to estimate canopy volume and the estimated volume is compared with the change on the backscattering coefficients of multi-temporal radar images. The process used in this study is useful to monitor the small change detection with high resolution radar data given by ALOS 2 satellite launch near the future.

In this study, changes of the L-band PALSAR polarimetric signal scattered from earthquake/tsunami damaged areas has been investigated. Since it is not always possible to get data sets having sufficiently good spatial/temporal resolutions to detect large area of damages precisely, it is of specific interest to use of wave polarization information appropriate to assess disaster-induced changes of scattering phenomena. Experimental results indicate that different polarimetric parameters and similarity measures provide complimentary information on damages. Consequently, it is important to utilize polarimetric features maximizing target changes and minimizing the false-alarm and the missed-alarm rates in developing damage assessment algorithms.

This paper proposes a simple man-made object detection method by utilizing the nonnegative eigenvalue decomposition (NNED) [1] and a unitary rotation [2] to the covariance matrix. It is observed from the result of the PolSAR image analysis using the proposed method that the double-bounce scattering contribution Pdbecomes stronger than that by the conventional scattering power decomposition even when the stricken residential houses are built on rough and slope ground surface. Furthermore, in order to analytically verify the improved effect for the stricken man-made object detection, the Finite-Difference Time-Domain (FDTD) polarimetric scattering analysis is also carried out for a simplified man-made objects model, which consists of quad dielectric rectangular parallelepipeds on finite circular plate with rough surface. The rectangular parallelepipeds are considered as a simplified man-made buildings model. It is cleared from the FDTD result that the enhancement of Pdcan be realized by doing the C22rotation, in addition to the NNED procedure.

The moisture content of terrain or forest, which is highly related to the permittivity, is a key parameter to understand global water cycle. This study is focused on retrieving permittivity from radar backscattering measurements. The basic concept of the proposal is to utilize Brewster's angle effect of double-bounce backscattering. If the Brewster's angle is known, it is possible to estimate the permittivity of medium directly. In order to demonstrate the Brewster's angle effects on the backscattering of forested area, the authors have carried out a simple laboratory experiment and the results are shown in this paper.

There exist 9 independent polarization parameters in the coherency or covariance matrix as the second order statistics. Various decomposition methods have been presented based on the physical scattering model using these parameters. However, none of them accounts for all polarimetric information, typically leaving T13element un-accounted in the coherency matrix. This paper presents a complete four-component scattering power decomposition method using all polarimetric information. Using double unitary transformation of measured coherency matrix, it is possible to eliminate T23element, which results in a reduction of polarization parameters from 9 to 7. Then by unitary transformation of expansion matrices, it becomes possible to account for T13term, which has never been accounted for in the physical model-based decomposition. By the double unitary transformations to minimize the T33component, all polarimetric parameters are accounted. This methodology also reduces the negative power problem significantly. This method is a further extension of the existing four-component decomposition. The four scattering powers (surface, double bounce, volume, helix) are assigned to blue, red, green, and yellow to compose full-color image. An example image of San Francisco area acquired with ALOS-PALSAR is shown to validate the decomposed result.

In the three- or four-component decompositions, polarimetric scattering properties and corresponding physical scattering models play essential roles for power decomposition. This letter proposes an improved four-component scattering power decomposition method that employs a suitable volume scattering model for single- or double-bounce scattering in the polarimetric synthetic aperture radar image analysis. The cross-polarizedHVcomponent is created by both single-bounce object (such as vegetation) and double-bounce structures (such as oriented building blocks). It has been difficult to discriminate these two objects (vegetation against oriented buildings) in the decomposed images since theHVcomponent is assigned to the volume scattering due to vegetation only. We propose to extend the volume scattering model suited for two physical scattering models. It is shown that a vegetation area and an oriented urban building area are well discriminated compared to those resulting from the implementation of the existing four-component scattering power decomposition.

A new approach is proposed for ship detection, based on the generalized optimization of polarimetric contrast enhancement (OPCE). The new approach consists of two steps. For the first step, the OPCE problem will be solved for obtaining optimal polarizations. In this way, the received powers of a sea area are suppressed. For the second step, we construct a function which not only contains the power term but also contains a term associated with the scattering mechanism. Three optimal coefficients will be obtained such that the variance of the function values of a sea clutter area is minimal. In this way, ships on the sea area are detected. Using two examples, we demonstrate the effectiveness of the proposed approach for ship detection.

This paper presents scattering power decomposition images of fully polarimetric synthetic aperture radar (SAR) data for disaster monitoring. Utilization of fully polarimetric data can derive full color images with red-green-blue color coding, red for the double-bounce power, green for the volume scattering power, and blue for the surface scattering power, for which each color brightness corresponds to the magnitude. Since disaster events cause the changes of each scattering power, it becomes straightforward for everyone to recognize the changes of the color in the polarimetric decomposed images provided time series data sets are made available. After applying the four-component scattering power decomposition to fully polarimetric image data sets acquired with the Advanced Land Observing Satellite (ALOS) Phased-Array-type L-band SAR (PALSAR), several images are presented for natural disaster monitoring of volcanic activity, snow accumulation, landslides, and tsunami effects caused by great earthquakes. It is seen in the polarimetric decomposition images that the surface scattering power becomes predominant in most disaster areas compared to those in normal situations.

ALOS PALSAR uses L band which is related with volume scattering. The volume scattering is the reflection comes from the surface and the inside of canopy. Forest management activities can be monitored using PALSAR L-band for Reducing Emissions from Deforestation and Forest Degradation (REDD). But it is still unknown how the biomass changed by the thinning (forest management) influences the backscattering coefficients. In this study, the change of stem volume (biomass) was computed and mapped using multi-temporal airborne lidar data in wide area. The stem volume using field measured tree parameters were interpolated and mapped by the airborne lidar data and the mapped biomass was used as the ground truth for radar image to see the polarization change caused by the thinning. The polarization axis of HV/HH backscattering coefficients is shifted in 9.8 degrees. This axis change is a useful indicator for the end member analysis using HV/HH polarization.

This study aims to evaluate the effects of topographic slope variation on the vegetation backscattering characteristics of polarimetric imaging radar. The experimental analysis of those parameters is carried out on the basis of the simulation of target matrix from the microwave vegetation scattering model. The relationship between polarimetric parameters and vegetation scattering mechanisms can be significantly affected by the presence of topography. Therefore, changes in vegetation scattering mechanisms, particularly in the vegetation-ground interaction terms, should be carefully taken into account in the analysis of SAR signal from mountainous areas.

This paper investigates polarimetric scattering characteristics from boundary region between the water area and the surrounding emerged plants in wetland, and proposes a simple true water area estimation method, by using both ΦHH- ΦVVand Pdin polarimetric synthetic aperture radar (PolSAR) im age analysis. The true water area includes not only the body of water but also the water area under emerged-plants and/or floating-leave plants in wetland. To verify the generating mechanism of the specific polarimetric scattering features, the Finite-Difference Time Domain (FDTD) polarimetric scattering analysis is also carried out for a simplified boundary model, which simulates the local boundary region around the wetland and consists of lots of vertical thin dielectric pillars on a dielectric plate. Taking into account the polarimetric feature obtained by both the FDTD and PolSAR image analyses, one can distinguish the actual water area from the emerged-plants around a wetland, even when the water area is concealed by emerged plants and/or floating-leave plants. Resultantly, it is found that by using the proposed simple classification markers, one can estimate the true water area boundary for wetland and the surrounding emerged-plants.

Extant airborne SAR has a lot of advanced function such as full polarimetry, multi-bands, etc. However it possesses several problems and has not been becoming practical use commonly in Japan. Therefore, the aim of our project is develop the following technologies aiming at the practical use of the airborne SAR. First, past flood disasters were investigated for function requirement arrangement and design equipped with the requested airborne SAR. Next, the SAR sensor (X-band) was improved based on the previous year's design, the proof experiment of the grasp technology such as ranges of the flood was conducted in the test field (Watarase storage basin) and the remaining issues for the real operation were revealed.

This investigation presents an improved methodology for decomposing the fully polarimetric SAR data based on the 4-Component Scattering Power Decomposition (4-CSPD) scheme. Using a phase rotation of 3×3 coherency matrix and the minimization of phase rotated matrix element T33, 4-CSPD model has been applied on fully polarimetric SAR images. This new decomposition methodology shows accurate decomposition of fully polarimetric SAR data over oriented urban area and mountainous area as compared the original 4-CSPD model.

This paper presents the comparison between the potential of SAR data in compact polarimetry (CP) and full polarimetry mode for snow detection. Eigenvalues based analysis and developed procedure have been used for this inter-comparison. It has been found over the part of rugged Siachen glaciated terrain, Indian Himalaya that dual polarization and compact polarization mode show-30% and-15% less capability than FP mode respectively.

In this tutorial presentation, model-based scattering power decompositions for fully polarimetric SAR data analysis are reviewed, and a relevant four-component scattering power decomposition scheme is presented. Although the method is still not perfect, it provides us with intuitive imaging results by RGB color-coding based on the physical scattering mechanisms. By using a rotation of the coherency matrix to minimize the cross-polarized component, the result of the four-component decomposition becomes more significant with simple and easy calculation. It is shown that oriented urban areas or man-made structures are clearly distinguished from volume scattering as double bounce objects.

On behalf of the Technical Program Committee (TPC), we would like to welcome you to the IGARSS 2011 in Vancouver, Canada, one of the most beautiful cities in the world, and to express my appreciation to the International TPC and AdCom members for their valuable contributions to the symposium.

In this letter, a censoring scheme that iteratively updates the outlier/target maps for target detection in synthetic aperture radar (SAR) images is proposed. For each iteration, any pixels that are indicated by the outlier map as outliers are rejected (censored out) from the clutter estimation. The resulting detected target map is then used as the new outlier map for the next iteration. This procedure is continued until there is no change to the target map, which is then output as the final detection result. The proposed scheme is generically applicable for target detection in both single-channel and multichannel SAR images. In our experiment, in particular, we tested the proposed method on both single-channel and polarimetric SAR data, and its effectiveness was successfully demonstrated.

In this letter, an unsupervised method for estimating the equivalent number of looks (ENL) in synthetic aperture radar (SAR) images is proposed. Assuming that the multiplicative noise in the SAR images is a unit mean random variable of Gamma distribution parameterized by the ENL, a lookup table between the ENL and the variance of the logarithm of the multiplicative noise is established. Then, for a SAR image, we first apply the logarithmic transform to make the multiplicative noise become additive. After that, the variance of the additive noise in the logarithmic image is estimated; finally, the ENL is obtained according to the estimated noise variance by the pre-established lookup table. The effectiveness of the proposed method is demonstrated with both simulated and real SAR images.

This paper presents an improvement to a decomposition scheme for the accurate classification of polarimetric synthetic aperture radar (POLSAR) images. Using a rotation of the coherency matrix to minimize the cross-polarized component, the four-component scattering power decomposition is applied to fully polarimetric SAR images. It is known that oriented urban area and vegetation signatures are decomposed into the same volume scattering mechanism in the previous decompositions and that it is difficult to distinguish vegetation from oblique urban areas with respect to the radar direction of illumination within the volume scattering mechanism. It is desirable to distinguish these two scattering mechanisms for accurate classification although they exhibit similar polarimetric responses. The new decomposition scheme by implementing a rotation of the coherency matrix first and, subsequently, the four-component decomposition yields considerably improved accurate results that oriented urban areas are recognized as double bounce objects from volume scattering.

Recently, the use of linear features for processing remote-sensing images has shown its importance in applications. Unfortunately, traditional linear feature detection methods rely heavily on the image's local information which makes them vulnerable to the presence of noise in the image. This problem becomes particularly difficult for synthetic aperture radar (SAR) image applications where SAR images are corrupted by speckle noise. In order to overcome this problem, we propose a novel method that processes the polarimetric synthetic aperture radar (Pol-SAR) images by combining the multiscale image analysis with polarimetric information in a new fashion. A two-scale approach is adopted here. On a coarse level, the coarse regions of the linear features are extracted by a curvelet transform from a speckle noise reduced image obtained by the polarimetric whitening filter. On a fine level, we develop a fuzzy polarimetric detector to accurately locate the linear features inside the regions. The effectiveness of the proposed method is demonstrated using simulated Pol-SAR data acquired from both EMISAR and Convair-580 systems.

This paper investigates polarimetric scattering features generated from man-made targets for accurately observing stricken residential area by making full use of quad. polarimetric SAR data set. First, we propose a simple improvement of the scattering power decomposition method by introducing a unitary rotation of the coherency matrix. It is found from the results of the POLSAR image analysis for a mountainous region including some stricken residential areas that by carrying out the rotation procedure, double-bounce scattering contribution can be enhanced even when the targets are oriented and/or set on small inclined ground plane. This peculiar scattering feature is utilized as a useful marker for precisely detecting man-made targets. To verify the generating and enhancing mechanism of the double-bounce scattering, we also carry out the Finite-Difference Time-Domain (FDTD) polarimetric scattering analysis for a simplified man-made target model. The dependency of the polarimetric scattering feature on the variation of squint angle is investigated. It is confirmed from the FDTD analysis for oblique squint angular range that by applying the rotation procedure before the scattering power decomposition, the double-bounce scattering contribution tends to be enhanced, and the volume scattering becomes small.

Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of spaceborne sensor dual-polarization C-band data of ENVISAT- ASAR can enhance the accuracy in measurement of snow physical parameters as compared to single polarization data measurement. This study shows the capability of C-band SAR data for estimating dry snow density over snow covered rugged terrain in Himalayan region. The snow density is an important parameter for the snow hydrology and avalanche forecasting related studies. An algorithm has been developed for estimating snow density, based on snow volume scattering and snow-ground scattering components. The radar backscattering coefficients of both HH and VV polarization and incidence angle are used as inputs in the algorithm to provide the snow dielectric constant which can be used to derive snow density using Looyenga's, semi empirical formula. Comparison was made between snow density estimated from algorithm using ENVISAT-ASAR HH and HH polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 24.38 kg/m3.

In this study, the capability assessment of fully polarimetric L-band ALOS PALSAR data has been carried out for snow discrimination from other targets. Eigenvaluve based polarization fraction value has been determined for assessing the capability of PALSAR data for snow discrimination. Radar snow index has been developed using polarization fraction and normalized third eigenvalue of coherency matrix. It has been found that radar snow index is more robust and simple to implement that supervised classification.

The scattering power decomposition for POLSAR data is one of the powerful tools in the radar polarimetry. There are several model-based decomposition techniques. However, since the number of independent observables in POLSAR images is limited, these techniques require several assumptions to obtain unique solution. The authors have proposed an alternative technique with POL-InSAR dataset. By using the POL-InSAR dataset, we can increase the number of observables. However, selection of volume scattering component was still a problem. Recently, Dr. Arii et. al., proposed a generalized volume scattering model, and applied it to the POLSAR dataset with the adaptive non-negative eigenvalue decomposition technique. In this report, we appy the model to the ESPRIT-based POL-InSAR decomposition technique and verify the estimation performance experimentally.

In this presentation, a new decomposition scheme of first using a rotation of the coherency matrix followed by the four-component decomposition is presented. It is shown using airborne Pi-SAR data sets that oriented urban areas are clearly distinguished from volume scattering as double bounce objects by the rotation of coherency matrix.

This paper proposes an accuracy improvement of the vegetation area classification based on the POLSAR image analysis, when vegetation and man-made areas are both included in the radar target region. Here we introduce a simple compensate polarimetric marker, T13or T31, to accurately distinguish the unexpected scattering from the obliquely oriented man-made targets and the complex volume scattering generated from vegetation area. T13or T31is the (1, 3) or (3, 1) elements of the 3×3 averaged coherency matrix ([T]), and has not been effectively utilized in the general scattering power decomposition scheme. By appropriately utilizing T13(T31), one can extract only the vegetation area from the mixed target region. It is verified from the detailed analysis that the proposed marker T13(T31) works well not only for Pi-SAR data but also for ALOS/PALSAR data.

This paper proposes a simple monitoring technique for water area change in wetland environment based on POLSAR image analysis. From the results of the image analysis by utilizing fully or quad. polarimetric SAR data set, it is found that strong double-bounce scattering can be observed at the vicinity of the boundary region between water area and the surrounding emerged-plants area. This peculiar scattering feature is utilized as a useful marker for estimating the water area change. To verify the generating mechanism of the double-bounce scattering, the Finite-Difference Time-Domain (FDTD) polarimetric scattering analysis is also carried out for a simplified water-emergent boundary model. The dependency of the polarimetric scattering feature on the variation of the radar incident angle, the change of the biomass and material parameters of the surrounding emerged-plants is investigated. Resultantly, it is confirmed from the detailed FDTD analysis that by using the proposed technique, one can estimate the true water area change in wetland and its surrounding area in all seasons.

The three-component scattering model decomposition based on the scattering mechanisms corresponding to single-bounce, double-bounce, and volume scattering, is one of the powerful tools to classify terrain for fully polarimetric SAR data. However, the conventional scattering component decomposition technique with POLSAR image requires several assumptions to obtain a unique solution. This paper presents scattering component decomposition technique with Pol-InSAR image pair. The proposed technique based on the ESPRIT can decompose without any assumptions when a suitable set of candidates for volume scattering covariance matrix is prepared. In this report, we show the concept of the proposed method with the POL-InSAR image pair and the role of the ESPRIT in the volume scattering estimation. Experimental results of SIR-C/X-SAR are also provided as an example.

The 2008 IEEE Geoscience and Remote Sensing Society's (GRS-S's) Awards were presented at the International Geoscience and Remote Sensing Symposium (IGARSS) 2008. These included IEEE Fellow Recognition; GRS-S Distinguished Achievement Award; GRS-S Outstanding Service Award; GRS-S Education Award; IEEE Electromagnetics Award; several Publication Awards and two Certificates of Recognition.

Land cover monitoring is one of the most potential applications of Polarimetric Synthetic Aperture Radar (POLSAR) sensing and so is Repeat-Pass Polarimetric-Interferometric SAR (RP-DIFF-POL-IN-SAR) stress-change assessment for air/high-altitude/space-borne SAR sensor deployment. Provided fully polarimetric SAR information can be made available, a plethora of novel POLSAR matrixde composition methods can be implemented for recovering rather precise scattering contributions from isolated and distributed scattering scenarios, and so can rather exact environmental changes from consecutive repeat-pass observations. With the recent launches of the fully polarimetric satellites JAXA-ALOS (PAL-SAR-L), the DLR TerraSAR-X and RADASAT-2 (C), a new era in space imaging of the terrestrial terrain and ocean surfaces has arrived providing unforeseen advantages. Whereas in the past, POLSAR applications were focused mainly on information product gathering for agriculture, forestry and the fisheries, little emphasis was placed on demonstrating its full capacity also for the assessment of natural habitats and especially wetlands. Therefore, it is essential to demonstrate how seasonal changes and features of vegetation in natural habitats, shallow vegetated lakes and wetlands can be recovered, provided fully polarimetric SAR image data takes can be made available for full polarimetric scattering matrix acquisition for which the standard symmetry condition HV=VH may not necessarily be sufficient.

This paper analytically examines polarimetric scattering for a simplified man-made structure model, by utilizing the finite-difference time-domain (FDTD) method. The model considered here simulates a building in mountainous environment and is composed of a dielectric rectangular parallelepiped on finite rough and/or inclined plate. The statistical FDTD analysis on two polarimetric indices, the scattered power decomposition and the polarimetric correlation coefficient, with respect to squint angle change is carried out. It is found from the detailed analysis that the considered polarimetric indices are valid even when the surface of the ground plate has small roughness and gentle slope. This result contributes to the development of Polarimetric Synthetic Aperture Radar (POL-SAR) image analysis for detecting or classifying man-made residential houses in mountainous region.

This paper presents a simple water area classification technique based on POLSAR image analysis and investigates the seasonal change of the true water area in wetland by making use of the satellite ALOS/PALSAR data, in addition to the air-borne Pi-SAR data. We here utilize the scattering power decomposition method in the image analysis, and verify that the double-bounce scattering, which can be considered as a true water area classification marker, is observed from the wetland and the surrounding emerged plants for both the airborne and satellite POLSAR data sets, even though the resolution and the incident angle are quite different. The dependency of the polarimetric scattering feature on the variation of the incident angle is also investigated by the polarimetric scattering analysis for a simplified emergent-water boundary model by using the finite-difference time-domain (FDTD) method. It is resultantly confirmed that the double-bounce scattering is utilized as a useful marker for the distinction of the true water area of the wetland even for small incident angle case.

We applied three-component (H//A) decomposition analysis and four-component (Ps, Pd, Pvand Pc) analysis to ALOS-PALSAR quad-polarized data of Izu-Oshima and Miyakejima volcanoes to monitor volcanic activities. The results demonstrated clear discrimination of forest, urban and volcanic deposit areas, indicating a high possibility of detecting land-cover changes at volcanoes by space-borne PolSAR observations.

This paper presents scattering model decomposition technique with Pol-InSAR data sets. The target model decomposition based on the scattering mechanisms corresponding to surface, double, and volume scattering, is one of the powerful tools to analyze and classify the ground surface conditions with fully polarimetric SAR data. However, we sometimes meet difficulty that the power of the decomposed component(s) becomes negative. This is caused by assumptions in the decomposition technique. The number of observables is limited in the fully polarimetric SAR data, therefore some assumptions are necessary. However, when an interferometric pair of fully polarimetric SAR data is available, additional observables can be obtained. These new observables will be available to resolve the difficulty of negative power in the conventional decomposition technique. In this paper, we consider the scattering model decomposition technique with Pol-InSAR data, and propose some modifications for the decomposition.

This paper presents a coherent decomposition scheme for a few scattering matrix data. A simple model-fitting decomposition method is applied to retrieve scattering mechanism or identify scatterers based on the coherency matrix. The decomposition results are compared with some experimental data obtained by fully polarimetric FM-CW radar. It is shown the scattering mechanisms are well recovered and the orientation angles of wire scatterer are precisely measured.

It is important to monitor environmental changes of the Earth's cover by remotely sensed data. This paper analyzes seasonal changes of a wetland by a modified polarimetric four-component scattering power decomposition method. The data sets analyzed here are L- and X-band fully polarimetric synthetic aperture radar (POLSAR) data, which have been acquired by the NICT/JAXA airborne polarimetric and interferometric synthetic aperture radar system in 2004. Since there existed a deficiency in the currently adopted decomposition schemes in that negative powers appear in a few pixels in the image analysis, we modified the approach taking into account physical conditions. It is shown by the modified scheme that the seasonal changes and features of the vegetation near Sakata Lagoon in Niigata, Japan, are observed clearly, demonstrating the utility of POLSAR image analysis for wetland assessments in general.

This report attempts to apply a hybrid classification technique based on POLSAR image analysis to detect the man made residential houses in a local mountainous area stricken by the Mid Niigata Prefecture Earthquake. The present hybrid scheme includes the scattered power decomposition method based on physical scattering nature and the scattering feature extraction method using polarimetric correlation coefficient. From the re sults of the image analysis for actual POLSAR data, it is verified that the present hybrid scheme provides us accurate classification of the man-made targets even in the severe mountainous area.

Presents the winners of the 2006 IEEE Geoscience and Remote Sensing Society's (GRS-S) Awards, including the IEEE Fellow Awards, the Distinguished Achievement Award, the Outstanding Service Award, the Education Award, and five Paper Awards.

A four-component decomposition scheme of the coherency matrix is presented here for the analysis of polarimetric synthetic aperture radar (SAR) images. The coherency matrix is used to deal with nonreflection symmetric scattering case, which is an extension of covariance matrix approach. The same decomposition results have been obtained. The advantage of this approach is explicit expressions of four scattering powers in terms of scattering matrix elements, which serve the interpretation of polarimetric SAR data quantitatively

For some special case, Huynen's decomposition cannot be used to extract a desired target from an average Kennaugh matrix. In this paper, the authors modify Huynen's method for overcoming its disadvantage, based on a simple transform of a Kennaugh matrix. Using an example, the effectiveness of the modified method is validated.

The polarimetric features of an oyster farm in a coastal area are analyzed to verify the applicability of radar polarimetry and interferometry. L-band Airborne Synthetic Aperture Radar (AIRSAR) data and Japan Earth Resources Satellite (JERS-1) data are used to examine the unique structure of an oyster farm located in South Korea. A specific feature of the oyster farm is the presence of numerous arrays of structures of various orientations that consist of exercise-bar-shaped poles protruding above sea level. This paper demonstrates that tide level is strongly correlated with the double-bounce scattering power from the vertical pole structures. This phenomenon is also verified by laboratory measurements using a network analyzer. In the laboratory experiment, double-bounce scattering and total power showed increasing trends with increased height of the vertical poles. Single-bounce scattering is sensitive to the orientation of horizontal poles relative to antenna orientation. HH-polarization is the most effective technique for imaging oyster farms from L-band polarimetric AIRSAR data. The authors were able to use a three-component decomposition of the AIRSAR data to distinguish an exposed tidal flat from a submerged tidal flat. The characteristics of the exposed tidal flat are similar to those of the carbon sponge in the laboratory test, except that the double-bounce scattering power is slightly greater in the real-world example. The single-bounce scattering component in AIRSAR data is generally greater than that in laboratory measurements because of sea-surface conditions and oyster growth. When the horizontal pole was aligned normal to the radar look direction, single-bounce scattering was greater than the double-bounce scattering, even under water-covered conditions. While a difference in tide height of 10 cm contributed approximately 3.0 dB in the laboratory experiment, a difference in tide height of 20 cm contributed to only approximately 1.7 dB in the JERS-1 SAR image intensity. JERS-1 SAR image intensity for areas dominated by double- and single-bounce scattering was 0.78 and 0.56, respectively. Results confirm that polarimetric SAR data are useful in selecting areas dominated by double-bounce scattering in oyster farms

A four-component scattering model is proposed to decompose polarimetric synthetic aperture radar images. The covariance matrix approach is used to deal with the non-reflection symmetric scattering case. This scheme includes and extends the three-component decomposition method dealing with the reflection symmetry condition that the co-pol and the cross-pol correlations are close to zero. Circular polarization power is added as the fourth component to the three component scattering model which describes surface, double bounce, and volume scattering. This circular polarization term is added to take account of the co-pol and the cross-pol correlations which generally appear in complex urban area scatttering and disappear for natural distributed scatterer.

A four-component scattering model is proposed to decompose polarimetric synthetic aperture radar (SAR) images. The covariance matrix approach is used to deal with the nonreflection symmetric scattering case. This scheme includes and extends the three-component decomposition method introduced by Freeman and Durden dealing with the reflection symmetry condition that the co-pol and the cross-pol correlations are close to zero. Helix scattering power is added as the fourth component to the three-component scattering model which describes surface, double bounce, and volume scattering. This helix scattering term is added to take account of the co-pol and the cross-pol correlations which generally appear in complex urban area scattering and disappear for a natural distributed scatterer. This term is relevant for describing man-made targets in urban area scattering. In addition, asymmetric volume scattering covariance matrices are introduced in dependence of the relative backscattering magnitude between HH and VV. A modification of probability density function for a cloud of dipole scatterers yields asymmetric covariance matrices. An appropriate choice among the symmetric or asymmetric volume scattering covariance matrices allows us to make a best fit to the measured data. A four-component decomposition algorithm is developed to deal with a general scattering case. The result of this decomposition is demonstrated with L-band Pi-SAR images taken over the city of Niigata, Japan.

This paper discusses the polarimetric correlation coefficient to extract the urban areas from polarimetric Synthetic Aperture Radar (POLSAR) image. For classification of POLSAR image, several methods have been proposed to extract polarimetric feature, such as Polarimetric Entropy-Alpha, three-component scattering model, Huynen parameters and so on. However, there is a possibility that the polarimetric correlation coefficient has a potential for the objective of this paper, too. In order to verify the capability of polarimetric correlation coefficient, we examine the behavior of this coefficient between the urban areas and the natural distributed areas with respect to the several polarimetric scattering models and the difference of polarization basis. Moreover, we apply the polarimetric correlation coefficient to the actual polarimetric SAR data acquired by Pi-SAR/X-SAR.

A generalized optimization of polarimetric contrast enhancement (GOPCE) is proposed in this letter. For this problem, it is not only necessary to find the optimal polarization states such that the received power ratio of a desired target and clutter is maximal, but also necessary to find three optimal coefficients such that the ratio of two factors associated with the desired target and clutter is maximal, where both the factors consist of three parameters, i.e., the Cloude entropy and two special similarity parameters. The optimal coefficients of the GOPCE are obtained by solving an eigenvalue problem. Using an example, we demonstrate that the GOPCE can be employed for detecting roads in a forest area by using polarimetric synthetic aperture radar data.

In this overview, reasons are provided on why we do need to place multi-modal, multi-band single and multiple pass POLinSAR monitoring platforms into air and space. The questions "on what POLinSAR monitoring can provide that POL-SAR and IN-SAR by themselves cannot accomplish" is assessed; whereupon facts and justifications on placing POL-IN-BISAR satellite clusters into space are presented. Reasons for this technology becoming a basic requirement for current, near-future and much more so for future all day & night year-round monitoring of the terrestrial covers are analyzed in view of the un-abating and uncontrollable terrestrial population explosion, which has, does and for ever will result in unavoidable conflicts deteriorating unfortunately at times into terrorism. The pertinent questions on how to reduce the exorbitant cost for initiating this "home-globe security protection" technology are therefore also broached, and the expected benefits are laid out. The pertinent National and International airborne and space borne multi-modal, multi-band SAR remote sensing and security conflict surveillance support agencies are herewith invited for co-sponsoring our proposal, which is timely and fleets of orbiting multi-band POLinSAR platforms are urgently required to be placed into space.

This paper tries to classify POL-SAR image data using polarimetric entropy, averaged alpha angle, and total power. Using unsupervised classification scheme with iterative Maximum Likelihood (ML) classifier, it is possible to classify residential areas with trees and vegetation areas. The results are shown using Pi-SAR image data comparing with other representative methods.

Abstract — This paper present a new forest height feature extraction technique for single baseline polarimetric and interferometric SAR data. The authors have proposed polarimetric SAR interferometry based on the ESPRIT algorithm. However, the algorithm assumes that there exist dominant polarized components in both the ground and canopy. Hence, estimated interferometric phase of local scattering centers may be slightly biased when forest components are highly depolarized. In this report, we examine effect of depolarized components of forest in ESPRIT-based polarimetric SAR interferometry. Numerical examples show that the effective scenario for the algorithm. Also, we present an alternative derivation of the ESPRIT-type algorithm which helps us understand how the ESPRIT algorithm works for depolarized components. Experimental results by using E-SAR data are also provided to show the performance of the ESPRIT estimation. Keywords-polarimetric SAR interferometry; forest canopy; ESPRIT algorithm; polarized and depolarized components I.

Polarimetric SAR interferometry has been widely studied for forest observations. The technique utilizes polarization state difference among local scattering centers of the forest to decompose them. Using the method, we can estimate precise forest parameters. We proposed an alternative method based on the ESPRIT algorithm for the estimation. The method can detect as many as 3 local scatterers with fully polarimetric data, and can extract interferometric phase of each local scatterer. In this paper, we verify the availability of the method in 2- and 3-local-scatterer model for forest estimation, and show that forest analysis with 3-local-scatterer improve height estimation accuracy. SIR-C/X-SAR data were used for these verifications. In addition, experimental results of restricted dual-polarization data are also provided to show availability of the method.

The ALOS-PALSAR is the first L-band space-borne SAR in the world equipped with a fully polarimetric data taking function. For the data utilization purpose, the authors have examined the polarimetric performance (scattering matrix and resultant images) over the Toyano area, Niigata City, Japan, using the airborne Pi-SAR radar. Since the resolution will be 20 m by 20 m on the ground surface by PALSAR, the Pi-SAR data (3 m by 3 m) are ensembled to be an equivalent size for ALOS-PALSAR image simulation. The polarimetric characteristics of terrain including paddy-fields, grass-fields, residential areas, etc., were examined to show the difference in the right-left polarization handedness basis and coherency vector, three-component scattering model, and polarimetric entropy.

Pacific Rim Campaign has been successfully carried out in 2000. The well known AIRSAR system by JPL flew over Niigata Area, Japan, on Oct. 2, 2000, which brought us the L-band fully polarimetric data set. On the other hand, Pi-SAR, developed by CRL and NASDA, Japan, simultaneously (2 hours in advance) flew the same path taking the same area data in the L-band. It became possible for us to carry out comparative analyses using the same area data. Since the resolution and incidence angle of each SAR are different, we examined the difference in the two POL-SAR images using correlation coefficient in the Right-Left Circular (RL) polarization basis, polarimetric entropy, and three component scattering power ratio.

Characteristic polarization state theory is restudied for the symmetric coherent Sinclair scattering matrix case. First, the geometric relations of the characteristic polarization states on the Poincare sphere are derived. Based on these relations, simple formulas are given for all of the characteristic polarization states of this Sinclair matrix in Stokes vector form. From the formulation, it is clear that the CO-POL Nulls are fundamental characteristic polarization states for the symmetric coherent Sinclair scattering matrix case, in that the others can straightforwardly be obtained from the Stokes vectors of the CO-POL Nulls. For further study of the characteristic polarization state and the distribution of the received powers on the Poincare sphere, the authors introduce the concept of the equi-power curve. It is defined as the curve on the Poincare sphere on which the received powers in some defined channel have the same value. They deal with the characteristics of the equi-power curves for various special cases. In addition, they show how the characteristic polarization states are generated by the equi-power curves. It is demonstrated that the characteristic polarization states can usually be regarded as the points of contact of the Poincare sphere and a conicoid representing a power-related quadratic form. This leads to a new method to introduce the characteristic polarization states.

In a co- or cross-polarized channel, the polarization states of the transmitting and receiving antennas are the same or orthogonal, and the corresponding target nulls (i.e., the co-pol nulls or x-pol nulls) are defined as the polarization states of the transmitting antenna such that the received power equals zero. However, no systematic studies have been carried out to solve the problem of the corresponding target nulls if the polarization states of the transmitting and receiving antennas are independent. In this paper, the target null theory is extended to the case of two independent polarization states. For two arbitrary independent symmetric scattering matrices, it is proved that there exists only one pair of polarization states such that both of the received powers equal zero. This polarization states' pair is called the co-null of the two targets, which can easily be obtained by solving an eigenvalue problem. Based on this concept and algebraic theory, the concept of the co-null space is introduced for the symmetric scattering matrix case, and many important results are presented, e.g., the relations between the co-null and the co-pol/x-pol nulls, the properties of the co-null space, and the relation between the co-null and target decomposition. Finally, the co-null for the asymmetric scattering matrix case is studied. The concepts of the mono-co-null space and the bi-co-null space are introduced, and the relations between both spaces are presented.

This paper proposes two effective numerical methods to solve the optimal problem of contrast enhancement for the coherent and incoherent cases, respectively. For the coherent case, the objective function of the optimal problem is transformed into a bilinear form. Then a numerical method is presented by using the idea of the Sequential Unconstrained Minimization Technique (SUMT). For the incoherent case, a cross-iterative method is proposed for solving the optimal problem of contrast enhancement, based on the formula of the optimal contrast polarization state in the matched-polarized channel. Both the proposed methods are convergent and straightforward for programming. In addition, the proposed methods can be used for the bistatic radar case because in this paper, it is unnecessary to restrict the symmetry of the scattering matrix and the Kennaugh matrix. For showing the effectiveness of the proposed methods, the authors give three examples. The results of the calculation are completely identical with other papers', showing the validity of the proposed methods. In these three examples, if the receiving polarization state is independent of the transmitting's, the power ratios may be much larger than those in the cases of the co-polarized, the cross-polarized, and the matched-polarized channels, showing the importance of the considered models. In addition, the computation costs are estimated by these illustrative examples, illustrating that the proposed methods are very effective.

The subsurface radar suffers from two typical problems, i.e., strong clutter from surface and severe wave attenuation in the underground. This paper presents a unique countermeasure to these problems using a polarimetric FM-CW radar and an equivalent sensitivity time control (STC) technique. The authors apply the polarimetric filtering principle to suppress surface clutter either in the Co-pol channel or in the X-pol channel of synthetic aperture radar, yielding to polarimetric enhanced target image. This technique works when the surface clutter and target have different polarization properties. Moreover, they use an equivalent STC technique specially suited for FM-CW radar for a deep object sounding to compensate wave attenuation within the ground. These techniques contribute to a significant improvement of the radar performance and the detection image contrast, although the detection of the target is in general a much more complicated topic. The field experiments were carried out to show the usefulness of the method. Some detection results are presented.

Presents experimental results of polarimetric detection of objects buried in a natural snowpack by a synthetic aperture FM-CW radar. First, the principle of polarimetric imaging in the Co- and Cross(X)-pol radar channels is outlined based on the scattering matrix and the characteristic polarization states for a specific target. Then, polarimetric measurements were carried out to detect objects buried in a natural snowpack 230 cm deep. The targets included two orthogonally placed metallic plates, an ice layer within the snowpack, and a human body. It is shown that the polarimetric FM-CW radar clearly enhances target signatures and that it serves to significantly improve detection in snowpack. Several polarimetric detection results are displayed, demonstrating the potential capability of characteristic polarization imaging and the usefulness of FM-CW radar.

This paper applies the principle of radar polarimetry to a synthetic aperture frequency-modulated continuous wave (FM-CW) radar and presents results based on two-dimensional (2-D) full polarimetric imaging. It is shown that the polarimetric target reflection coefficients obtained by the synthetic aperture FM-CW radar are elements of a Sinclair scattering matrix, although the coefficients are derived from a wide band signal. Using the scattering matrix optimization procedure, a 2- D polarimetric imaging experiment (including Co-Pol maximum, minimum, span, and phase imaging) of an orthogonally placed linear target set was successfully carried out in the laboratory. This result demonstrates the validity of S-band (8.2-9.2 GHz) FM-CW radar polarimetry, and it presents a demonstration of a full polarimetric 2-D FM-CW imaging radar system

Presents the principle of synthetic aperture frequency modulated continuous wave (FM-CW) radar and demonstrates the detection results of several objects buried in natural snowpack using the radar system. First, the synthetic aperture technique is explained with emphasis placed on showing that the Fourier transformed beat signal obtained by the FM-CW radar is equivalent to one kind of Fresnel hologram, which leads the authors to use the SAR technique. Then a radar system operative in the microwave L-band is explored to detect objects buried in natural snowpack. Several detection results are presented demonstrating the potential capability of high resolution imaging in the azimuth direction, comparing with real aperture images.>

The characteristic polarization states in the cross-polarized radar channel for the monostatic reciprocal case is derived based on a Stokes vector formulation. The optimization procedure for the channel power leads to an eigenvalue equation which explains the characteristic polarization state properties mathematically and physically. The method is illustrated for a hypothetical target.>

An FM-CW radar system was applied to detect a human body buried in a very wet snowpack. This radar uses the L-band microwave frequency with a maximum output power of 100 mW, and utilizes digital signal processing techniques. Field experiments were carried out to detect and map a human body embedded at a depth of 125 cm in a natural snowpack. The radar is shown to have a potential ability to detect avalanche victims, indicating that it may become a tool for snow rescuer operations.>

Basic principles of radar polarimetry are introduced. The target characteristic polarization state theory is developed first for the coherent case using the three stage, the basis transformation, and the power (Mueller) matrix optimization procedures. Kennaugh's and Huynen's theories of radar target polarimetry are verified for the monostatic reciprocal case. It is shown that there exist, in total, five unique pairs of characteristic polarization states for the symmetric scattering matrix of which two pairs, the cross-polarization null and copolarization max pairs, are identical, whereas the cross-pol max and the cross-pol saddlepoint pairs are distinct. The theory is verified by an example for which next to the polarization fork the copolarized and cross-polarized power density plots are also presented. The partially polarized case for completely polarized wave incidence is presented and compared with the results for the coherent and the partially coherent cases, the latter of which is still unresolved.>

A real-aperture FM-CW radar system was developed for the detection of objects buried in heavily wet snowpack. This radar uses the L-band microwave frequency with a maximum output power of 100 mW and utilizes digital signal processing techniques. A laboratory simulation and two field experiments were carried out to detect and map various objects embedded in the snowpack. It was possible to map a metallic pipe 3 cm in diameter at a depth of 70 cm and a 10*60-cm metallic plate at a depth of 90 cm in natural snowpack. Fundamental detection results are presented.>