Abstract:A filtering algorithm is proposed that accurately extracts ground data from airborne light detection and ranging (LiDAR) measurements and generates an estimated digital terrain model (DTM). The proposed algorithm utilizes planar surface features and connectivity with locally lowest points to improve the extraction of ground points (GPs). A slope parameter used in the proposed algorithm is updated after an initial estimation of the DTM, and thus local terrain information can be included. As a result, the proposed algorithm can extract GPs from areas where different degrees of slope variation are interspersed. Specifically, along roads and streets, GPs were extracted from urban areas, from hilly areas such as forests, and from flat area such as riverbanks. Validation using reference data showed that, compared with commercial filtering software, the proposed algorithm extracts GPs with higher accuracy. Therefore, the proposed filtering algorithm effectively generates DTMs, even for dense urban areas, from airborne LiDAR data.

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In this letter, an algorithm is proposed that robustly extracts urban areas from polarimetric synthetic aperture radar images. Polarization orientation angle (POA), volume scattering power (Pv) derived by four-component decomposition, and total power (TP) are utilized in the proposed algorithm. The dependence of the four decomposition components on POA can be lessened by rotating the elements of the coherency matrix by the POA. However, a level of POA dependence remains even after the correction. The proposed algorithm utilizes POA-corrected components, but pixels are grouped into several categories according to POA. First, urban and farmland training data are selected for each category in a study area. Then, urban and mountain areas are separated from farmland, bare ground, and sea by utilizing the Pv-TP scattergram. Finally, a measure of the POA randomness between neighboring pixels is used to discriminate between urban areas with nearly homogeneous POA and mountain areas with randomly distributed POAs. When performing classification on more than one study area, thresholds manually selected for one of the study areas are used to automatically estimate thresholds for the other areas. An accuracy assessment demonstrates that POA-based categorization and utilization of POA randomness contribute to improving classification accuracy.Index Terms-Four-component decomposition, polarimetric synthetic aperture radar (SAR), polarization orientation angle (POA), urban-area extraction.

The garnet‐perovskite phase transformation in CaGeO3 was investigated in the pressure‐temperature region to 6.5 GPa and 1200°C using a cubic anvil type of high‐pressure apparatus combined with synchrotron radiation. In‐situ measurements with an energy dispersive x‐ray diffraction system enable us to carry out dynamical observation of the transformation. The equilibrium phase boundary between the garnet and perovskite phases was determined as P(GPa)= 6.9 ‐ 0.0008T(°C). The negative P‐T slope definitely established in the present study is in reasonable agreement with the value, −0.0023(8) GPa/°C, that was calculated from the thermochemical data on the enthalpy of transition. The molar volume change accompanied with this transformation was estimated to be about 13% at about 6 GPa and 1000°C.