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The polarimetric synthetic aperture radar (PolSAR) provides us with a two-by-two scattering matrix data set. The ensemble averaged coherency matrix in an imaging window derived using a scattering matrix has all non-zero elements in its three-by-three matrix. It is a full 3 × 3 matrix that bears nine real-valued and independent polarimetric parameters inside. In the proposed decomposition method, G5U, we preprocess observed coherency matrix [T] by using two consecutive unitary transformations to become an ideal form for five-component decomposition. The transformation reduces nine parameters to seven, which is the best fit for five-component scattering model expansion. We can retrieve five powers corresponding to surface scattering, double bounce scattering, volume scattering, oriented dipole scattering, and compound dipole scattering, directly. These powers can be calculated easily and used to display superb polarimetric RBG images as never before, and are further applicable for polarimetric calibration, classification, validation, etc.

A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the forest classification accuracy and helps to realize rapid deforestation detection because dual-polarization data are more frequently acquired than POLSAR data. The proposed method involves constructing scattering power models for dual-polarization data considering the radar scattering scenario of tropical forests (i.e., ground scattering, volume scattering, and helix scattering). Then, a covariance matrix is created for dual-polarization data and is decomposed to obtain three scattering powers. We evaluated the proposed method by using simulated dual-polarization data for the Amazon, Southeast Asia, and Africa. The proposed method showed an excellent forest classification performance with both user’s accuracy and producer’s accuracy at >98% for window sizes greater than 7 × 14 pixels, regardless of the transmission polarization. It also showed a comparable deforestation detection performance to that obtained by POLSAR data analysis. Moreover, the proposed method showed better classification performance than vegetation indices and was found to be robust regardless of the transmission polarization. When applied to actual dual-polarization data from the Amazon, it provided accurate forest map and deforestation detection. The proposed method will serve tropical forest monitoring very effectively not only for future dual-polarization data but also for accumulated data that have not been fully utilized.

Pi-SAR2 is an airborne polarimetric synthetic aperture radar operated by the National Institute of Information and Communications Technology. The polarimetric observation data of Pi-SAR2 are very valuable because of its high resolution, but it cannot be used effectively because the data are not well calibrated with respect to elevation. Therefore, we have calibrated the data according to the observation conditions. The Pi-SAR2 observation data are very large due to its high resolution and require sufficient computational resources to be calibrated. We utilized the AI Bridging Cloud Infrastructure (ABCI), constructed and operated by the National Institute of Advanced Industrial Science and Technology, to calculate them. This paper reports on the calibration, scattering power decomposition, and orthorectification of the Pi-SAR2 observation data using the ABCI.

During influenza epidemics, Japanese clinicians routinely conduct rapid influenza diagnostic tests (RIDTs) in patients with influenza-like illness, and patients with positive test results are treated with anti-influenza drugs within 48 h after the onset of illness. We assessed the vaccine effectiveness (VE) of inactivated influenza vaccine (IIV) in children (6 months-15 years old, N = 4243), using a test-negative case-control design based on the results of RIDTs in the 2018/19 season. The VE against influenza A(H1N1)pdm and A(H3N2) was analyzed separately using an RIDT kit specifically for detecting A(H1N1)pdm09. The adjusted VE against combined influenza A (H1N1pdm and H3N2) and against A(H1N1)pdm09 was 39% (95% confidence interval [CI], 30%-46%) and 74% (95% CI, 39%-89%), respectively. By contrast, the VE against non-A(H1N1)pdm09 influenza A (presumed to be H3N2) was very low at 7%. The adjusted VE for preventing hospitalization was 56% (95% CI, 16%-77%) against influenza A. The VE against A(H1N1)pdm09 was consistently high in our studies. By contrast, the VE against A(H3N2) was low not only in adults but also in children in the 2018/19 season.

Background: Catheter ablation of non-paroxysmal atrial fibrillation (non-PAF) is a therapeutic challenge especially in elderly patients. This study describes the feasibility of a posterior left atrium isolation as a substrate modification in addition to pulmonary vein isolation, the so-called Box isolation, for elderly patients with non-PAF. Methods: Two hundred twenty-nine consecutive patients who underwent Box isolations for drug-refractory non-PAF were divided into two groups according to their age; younger group comprising 175 patients aged

This paper addresses the local incidence angle dependence of several polarimetric indices corresponding to landslides in forested areas. Landslide is deeply related to the loss of human lives and their property. Various kinds of remote sensing techniques, including aerial photography, high-resolution optical satellite imagery, LiDAR and SAR interferometry (InSAR), have been available for landslide investigations. SAR polarimetry is potentially an effective measure to investigate landslides because fully-polarimetric SAR (PolSAR) data contain more information compared to conventional single- or dual-polarization SAR data. However, research on landslide recognition utilizing polarimetric SAR (PolSAR) is quite limited. Polarimetric properties of landslides have not been examined quantitatively so far. Accordingly, we examined the polarimetric scattering properties of landslides by an assessment of how the decomposed scattering power components and the polarimetric correlation coefficient change with the local incidence angle. In the assessment, PolSAR data acquired from different directions with both spaceborne and airborne SARs were utilized. It was found that the surface scattering power and the polarimetric correlation coefficient of landslides significantly decrease with the local incidence angle, while these indices of surrounding forest do not. This fact leads to establishing a method of effective detection of landslide area by polarimetric information.