There is potentially strong demand for detailed 3D urban spatial data especially for advanced automobile/pedestrian guidance system and radio disturbance analysis for telecommunication and so forth. Ground-based laser range scanner is one of the promising devices to efficiently acquire detailed 3D spatial data such as facade of buildings and detailed urban objects along streets. From a single laser range image, however, entire surface of an urban object can not be covered due to occlusions. Multiple range images acquired from different locations and with different angles have to be registered with each other to minimize occlusions and to generate more complete 3D models. In this paper, a robust method for registering laser range images of an urban object like buildings is proposed under the assumption that the laser scanner is located so that the rotating axis is vertical to the ground. Registration is achieved in two steps; a pairwise registration of successive range images and simultaneous registration of multiple range images to adjust errors accumulated in the pairwise registration process. Pair-wise registration method to determine 4 transformation parameters, a horizontal rotation angle and three translation parameters is based on “Z-image”, which is generated by projecting 3D points computed from a range image onto the horizontal (x-y) plane. In the simultaneous registration of multiple images, accumulation of estimation error in pairwise registration is adjusted with a least square minimization method. An outdoor experiment is conducted to reconstruct a 3D model of the buildings in the campus of the University of Tokyo using twenty-nine range images. With the registration method, around twenty-three pairs of range images were automatically registered, while six pairs were registered with a manually assigned initial transformation. A 3D model of the buildings was generated in an automated mode. Accuracy of the model was examined using a 1: 500 scale digital map and GPS measured location of viewpoints.
A method for selection of appropriate band combinations for improvement of Sea Surface Temperature (SST) estimation accuracy is proposed. It is found that the proposed method for selection of appropriate band combinations for maximizing SST estimation accuracy using radiative transfer code, MODTRAN-3.7 in this case is useful to investigate a most appropriate SST retrieval algorithm as well as choosing an additional band to the existing band combinations. In comparison between the band combination of the existing NOAA/AVHRR and the appropriate band combination derived from this study, it is found that 8 to 10% of improvement on SST estimation accuracy can be achieved. Also it is found that the center wavelength of the first to fourth bands of the appropriate combinations are 935cm-1, 830, 1215, 2705cm-1, respectively. In this study, a variety of regression equations are tried. It is found that the best regression equation contains the term of the difference of the brightness temperature of the different bands, with and without water vapor absorption as well as the term of air-mass considerations. In comparison between the best regression equation and the regression equation without consideration of the aforementioned water vapor absorption and air mass, it is found that approximately 1.5% of improvement can be achieved for the best equation.
The Kyoto protocol was adopted in 1997. According to some definitional options, forest and non forest area are distinguished using crown cover ratio. In order to delineate such forest regions, the development of the way to measure the crown cover ratio of forest is urgently necessary. In this study, various methods were tested for estimating crown cover ratio by using airborne spectral sensor data. Negative correlation coefficients were found among spectral bands of CAST in visible region and crown cover ratio. The best correlation coefficient was attained by the ratio of between 465nm and 700nm bands. Standard error was around 5% of the correlation. Whereas, the correlation coefficient was low by the NDVI which used often to estimate vegetation biomass. This research shows that the possibility to estimate the crown cover ratio accurately with optical remote sensing data.
The authors evaluated the actual performance of some kinds of regression models for correcting image distortion due to surface topography in various kinds of satellite images including SAR data. For optical sensor images like SPOT/HRV, it was verified that a simple regression model in which only one height term is added to affine or pseudo-affine transformation can be applied with the error of about one pixel. On the other hand SAR data by RADARSAT resulted in the error twice of that for optical data, which was considered due to a difficulty in tie point selection between SAR and optical sensor images. Next, this technique was extended to height measurement. A case study with a SPOT panchromatic stereo pair verified that the similar regression model as that for height correction is possible to be applied for height measurement and the partial revision of a conventional DEM was attempted by using this regressive method.
An error budget analysis for the reflectance based vicarious calibration method is conducted. BRDF of the standard plaque for the reference of the surface BRDF measurement is measured and corrected in the calculation of Top of Atmosphere (TOA) radiance in the method. Sunphotometer for optical depth measurement is calibrated and corrected in the calculation of TOA radiance. It is found that approximately 3.9% of root sum square error can be achieved for the reflectance based vicarious calibration method.