With the spread of the digital cameras with integrated the zoom lens, in particular long range such as ×30, digital close range photogrammetry using the cameras is enormously expected in various application fields. In these circumstances, the authors have been concentrating on developing a calibration model for the zoom lens, and the model shown the capability to perform camera calibration with stable for all zoom setting. However, there is still problem for effective digital photogrammetry using the cameras, in particular close range photogrammetry using digital zoom setting. The main problem is instability of calibration procedure in digital zoom setting. In order to resolve instability of digital zoom setting, and practical use of digital close range photogrammetry by digital zoom setting, performance evaluation of digital zoom setting was conducted using 3 kinds of digital camera with integrated the zoom lens (×3.6～30) with digital zoom setting (×4～6.25) in this paper.
This paper describes an estimation method for crop conditions using Lasso (Least Absolute Shrinkage and Selection Operator) regression applying to airborne hyperspectral data. Lasso regression, which uses the multiple linear regression analysis with regularization, is suitable for analysis of a fewer measurement dataset with huge number of bands. The conventional estimation method of Lasso regression tends to select the regression model with an excessive number of bands. In addition, noises included in hyperspectral data have the potential to affect the Lasso regression model building. A method proposed in this paper achieves an improvement of the Lasso regression model building by using the moving average applied to hyperspectral data and Akaike's Information Criterion (AIC) applied in the estimation procedure for the regularization parameter. In the results of estimating the rice growth status using the airborne hyperspectral sensor AISA, Lasso regression with AIC accomplished to select more appropriate bands though the resultant correlation coefficient might be nearly equal to the one given by conventional method using the criterion of mean squared error with regularization. By adopting the normal distribution function with adjustable standard deviations for the window function in the moving average, it is able to exhibit various noise reduction levels. Our method currently proposed demonstrates that the optimal regression model for the hyperspectral data under noisy condition is necessary to have an appropriate moving average.
The archipelagoes in Philippine were selected to discuss a possibility to detect biological films by the Synthetic Aperture Radar (SAR). The ScanSAR Mode and the Fine mode of the ALOS PALSAR data were applied to represent the sea surface roughness. The distributions of chlorophyll-a concentration observed by the MODIS were used to discuss a contribution of biological films. The reanalysis wind data from the National Centers for Environmental Prediction (NCEP) were applied to distinguish wind fields of observation site. The Fast Fourier Transform (FFT) was applied to the surface roughness image observed by SAR to identify a directional component of wind wave and a power distribution in the frequency domain. A detection matrix of the biological films was created from the power spectrum distribution, chlorophyll-a, wind data, and discussed for the detection of biological films. From the interaction among chlorophyll-a concentration, surface winds, and sea surface roughness, the fine SAR image was more sensitive than the ScanSAR image for the detection of biological films.
The estimation of global solar radiation (GSR) is important for the studies on photovoltaics, building design, and agriculture. In this study, we developed a GIS-based method for estimating hourly GSR. Firstly, this method estimated clear-sky GSR using a solar radiation model, which is integrated within the open source environment of the GIS. Then, a GSR estimating equations were derived from a maximum likelihood estimation between GSR observed by Japan Meteorological Agency (JMA) and some parameters ; clear-sky GSR, cloudiness, relative humidity, and surface pressure. This analysis utilized the cloudiness, relative humidity and surface pressure of numeric prediction data, which was calculated by JMA using mesoscale model. It was found that the GSR estimating equations should be derived for each month using monthly data and setting thresholds of a solar elevation because the effects of the cloudiness, relative humidity, and surface pressure on the atmospheric attenuation vary with a solar elevation. The monthly root mean square errors ranged approximately from 0.20 to 0.53 MJ/m2.
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