Toshifumi Moriyama

Four-component scattering model for polarimetric SAR image decomposition

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.

Forest Structure Dependency of the Relation Between L-Band

Biophysical parameters and L-band polarimetry synthetic aperture radar observation data were taken for 59 test sites in Tomakomai national forest, which is located in the northern part of Japan. Correlations between the derived sigma_HH ⁰, sigma_HV ⁰, and sigma_VV ⁰ and the biophysical parameters are investigated and yield the following results. 1) The above-ground biomass-sigma⁰ curves saturate above 50 tons/ha for sigma_VV ⁰, 100 tons/ha for sigma_HH ⁰, and over 100 tons/ha for sigma_HV ⁰ when all forest species are included in the curves. 2) The sigma_HH ⁰-above-ground biomass curve for one forest species indicates a higher saturation level than that for the other forest species. Dependence on the forest species was absent for VV polarization and low for HV polarization. 3) A simple three-component scattering model indicates that volume scattering accounts for 80%-90% when the above-ground biomass exceeds 50 tons/ha. The surface-scattering components are up to ~20% for young stands, and the volume-scattering components are down to 70%. The origin of the dependency among the forest species was examined for the sigma_HH ⁰-above-ground biomass. It is concluded that a possible cause of the dependency is the different characteristics of the stands rather than forest species

sigma^0

To investigate the possibilities of using dual-frequency, multipolarization synthetic aperture radar (SAR) data to monitor sea ice, we derived the relationship between various polarization characteristics and the physical parameters of sea ice. We discuss the frequency and polarization characteristics of the backscattering coefficients of sea ice and then characterize its thickness by comparing the corresponding backscattering coefficient for each polarization with the physical parameters of the ice. We first propose a methodology for classifying sea-ice types by using a polarimetric decomposition technique, before comparing an estimation of the sea-ice thickness with the corresponding dual-frequency, multipolarization SAR data. We utilized the backscattering ratio to estimate the thickness of the sea ice. This ratio canceled the effect of roughness on the backscattering. The method was validated using Pi-SAR (polarimetric and interferometric airborne SAR) observation data obtained at ground-truth sites.

and Biophysical Parameters

This paper describes a polarimetric feature extraction method from urban areas using the POLSAR (Polarimetric Synthetic Aperture Radar) data. The scattering characteristic of urban areas is different from that of natural distributed areas. The main point of difference is polarimetric correlation coefficient, because urban areas do not satisfy property of azimuth symmetry, = 0. The decomposition technique based on azimuth symmetry can not be applied to urban areas. We propose a new model fit suitable for urban areas. The proposed model fit consists of odd-bounce, even-bounce and cross scattering models. These scattering models can represent the polarimetric backscatter from urban areas, and satisfy ¬= 0 and ¬= 0. In addition, the combination with the proposed model fit and the three component scattering model suited for natural distributed areas is examined. It is possible to apply the combined technique to POLSAR data which includes both urban areas and natural distributed areas. The combined technique is used for feature extraction of actual X-band POLSAR data acquired by Pi-SAR. It is shown that the proposed model fit is useful to extract polarimetric features from urban areas.

Observation of sea-ice thickness in the sea of Okhotsk by using dual-frequency and fully polarimetric airborne SAR (pi-SAR) data

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.

Polarimetric SAR Image Analysis Using Model Fit for Urban Structures

The Bayesian retrieval of sparse scatterers under multifrequency transverse magnetic illuminations is addressed. Two innovative imaging strategies are formulated to process the spectral content of microwave scattering data according to either a frequency-hopping multistep scheme or a multifrequency one-shot scheme. To solve the associated inverse problems, customized implementations of single-task and multitask Bayesian compressive sensing are introduced. A set of representative numerical results is discussed to assess the effectiveness and the robustness against the noise of the proposed techniques also in comparison with some state-of-the-art deterministic strategies.

A study on extraction of urban areas from polarimetric Synthetic Aperture Radar image

An inverse scattering approach based on the field equivalence principle is developed for reconstructing the parameters of the medium of a scattering object. A problem equivalent to the original scattering problem but internal to the measurement surface is set up. The equivalent surface currents determined by the measured total-field data rigorously yield the incident fields in the region under test, so the approach does not require explicit knowledge of the incident fields. Taking into account the fact that the equivalent surface currents produce a null field external to the surface, a functional of the mediums parameters is introduced, and a genetic algorithm is applied to minimization of the functional. Numerical simulations for imaging defects in a known dielectric cylinder from only total-field data measured on an observation surface are performed to illustrate the efficacy of the proposed inversion method.

Multifrequency Bayesian compressive sensing methods for microwave imaging.

This paper summarizes the initial PALSAR calibration and validation results, which were being carried out after the first activation of the PALSAR image on Feb. 15 2006. The PALSAR calibration and validation consists of the sensor characterization, SAR processor tuning, and image quality evaluation. During the three month initial calibration phase and two month initial calibration phase, sensor characterization through the raw data evaluation for most of the sensor modes were conducted for interpretation of the performance. In this paper, we focus on the results that were gained during first 7 months after the ALOS launch. Although three months remained by the ALOS operation start, the report may cover almost of the PALSAR CAL/VAL.

Inverse scattering approach based on the field equivalence principle: inversion without a priori information on incident fields

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.

PALSAR initial calibration and validation results

The purpose of this letter is to present the results on the study of searching effective parameters that describe the relation between high-resolution synthetic aperture radar (SAR) images and forest parameters. The study is based on the non-Gaussian texture analysis of the polarimetric airborne Pi-SAR data over coniferous forests in Hokkaido, Japan. The radar cross section (RCS) in terms of a forest biomass is first analyzed. It is found that the L-band RCS increases steadily with the biomass and saturates at approximately 40 tons/ha. These results are similar to the previous studies. The probability density function of the image amplitude is then investigated, and among Rayleigh, log-normal, Weibull, and K-distributions, the K-distribution is found to fit best to the L-band data of all polarizations, although the Weibull distribution fits equally well. Further, the correlation between the tree biomass and the order parameter of the K-distribution in the cross-polarization images is found to be very high, and the order parameter increases consistently with the biomass to approximately 100 tons/ha, which is well beyond the saturation limit of the L-band RCS. Thus, the order parameter of the K-distribution can be a promising new parameter to estimate the forest biomass from high-resolution polarimetric SAR data in a much wider range than the conventional RCS method