Naoto Usami

PolSAR Wet Snow Mapping With Incidence Angle Information

Polarimetric synthetic aperture radar is expected to distinguish wet snow from bare ground. However, since both of them show surface scattering, which is sensitive to incidence angle, it often fails in the distinction in mountainous areas. In this letter, we propose an adaptive distinction method using quaternion neural networks. In the ALOS-2 data, we find a monotonic and nonlinear dependence of the degree of polarization on the incidence angle. Then, we feed multiple-incidence-angle teacher information in the learning process. The distinction results of the proposal present higher accuracy than those of the conventional Wishart distinction and a quaternion neural network without the incidence angle information.

Proposal of adaptive land classification using quaternion neural network with isotropic activation function

Previously, we have proposed a successful land classification method using a quaternion neural network (QNN) to process parameters based on Stokes vector representation. In this method, the activation function used in the QNN is anisotropic and is applied to input quaternions of which elements are separately and independently processed. In this paper, considering the isotropy of Poincare-sphere space, we propose a new isotropic activation function. Experimental results show that the QNN with the proposed activation function achieves more accurate land classification.

Fabrication of PbS quantum dots and silicon device for near-infrared detection

Imaging devices for near-infrared wavelengths on silicon large scale integration (LSI) are very attractive for secure applications. We here demonstrate an LSI-compatible hybrid IR detector by integrating PbS quantum dots the surface of silicon.

An arrayed test structure for transistor damage assessment induced by circuit analysis and repairing processes with back-side-accessing Focused Ion Beam

We propose an arrayed test structure to assess the damages of metal-oxide-semiconductor field-effect transistors (MOSFETs) exposed under back-side LSI processes, such as by Focused Ion Beam (FIB). Back-side process with FIB is becoming essential to analyze and repair modern LSI chips, to avoid processing through many metal layers with dense wiring and dummy patterns. To access transistors from back-side, however, FET active region must be cropped out and that may cause damage to transistor characteristics. Our test structure consists of 2-D-arrayed MOSFETs. The impact by the back-side process on various conditions can be visualized as I–V characteristics change. The test structure was used with several FIB back-side processes and visualized the damages as threshold shift. The measurement indicated the importance of mixture of fast-and-isotropic etching and slow-and-anistoropic etching to miminimize electrical damage.

Proposal of wet snowmapping with focus on incident angle influential to depolarization of surface scattering

In this paper, we propose an effective wet snow mapping method with focus on the incident angle of microwave. Surface scattering is dominant for both wet snow and bare ground. However, it is expected that the characteristic of the wet snow scattering is different from the bare ground one according to the variation of dielectric constant. At the same time, surface scattering characteristics, especially depolarization, also depend on the incident angle. First, we evaluate numerically the degree of polarization of horizontal incident wave as an example with a simplified integral equation model (IEM). We also examine real data of full polarimetric synthetic aperture radar (PolSAR). The results shows that the degree of polarization depends on the difference of incident angles rather that of dielectric constants. Then we conduct wet-snow mapping by supervised learning with teacher areas for large / small incident angles and snow / bare ground. The mapping result agrees well with the estimation by optical data. It is found important to take into account the incident angle in snow mapping.