Simplifying of DLT Equations for a Single Digital Image and Its Application in Architectural Façades Documentation

Survey of architectural façades to obtain elevation drawings is an essential, especially in case of maintenance, restoration, ...etc. On the other hand, the rapid progress of the obtained image size captured by digital cameras opens new areas for the captured images to be used in photogrammetry. One of these new areas is the use of a single digital image for surveying and recording of architectural façades. So, the main objective of the current research is to develop a computer algorithm using least squares adjustment method for studying the practical visibility, applicability, and accuracy of using a single digital image captured by a digital camera in surveying architectural façades. To achieve the above-mentioned goal, simplified formulas obtained from collinearity condition, the basis of the Direct Linear Transformation model (DLT), to suit the architectural façades conditions, which is the façade lies in one vertical plane. The obtained formulas showed that eight transformation parameters are required (needed) between the architectural façade and the captured image. Hence, the eight parameters can be computed using four common points or more. So, two field experiments were made on two architectural façades to test the practical visibility, applicability, and accuracy of the supposed technique. The obtained results proved the success of the supposed technique and its related computer algorithm in the survey and the record of the vertical architectural façades.

Efficient estimation of a linear transformation model for current status data via penalized splines

We propose a flexible and computationally efficient penalized estimation method for a semi-parametric linear transformation model with current status data. To facilitate model fitting, the unknown monotone function is approximated by monotone B-splines, and a computationally efficient hybrid algorithm involving the Fisher scoring algorithm and the isotonic regression is developed. A goodness-of-fit test and model diagnostics are also considered. The asymptotic properties of the penalized estimators are established, including the optimal rate of convergence for the function estimator and the semi-parametric efficiency for the regression parameter estimators. An extensive numerical experiment is conducted to evaluate the finite-sample properties of the penalized estimators, and the methodology is further illustrated with two real studies.

A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI

A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI Timely and accurate earth observation with short revisit interval is usually necessary, especially for emergency response. Currently, several new generation sensors provided with similar channel characteristics have been operated onboard different satellite platforms, including Sentinel-2 and Landsat 8. Joint use of the observations by different sensors offers an opportunity to meet the demands for emergency requirements. For example, through the combination of Landsat and Sentinel-2 data, the land can be observed every 2–3 days at medium spatial resolution. However, differences are expected in radiometric values (e.g., channel reflectance) of the corresponding channels between two sensors. Spectral response function (SRF) is taken as an important aspect of sensor settings. Accordingly, between-sensor differences due to SRFs variation need to be quantified and compensated. The comparison of SRFs shows difference (more or less) in channel settings between Sentinel-2 Multi-Spectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI). Effect of the difference in SRF on corresponding values between MSI and OLI was investigated, mainly in terms of channel reflectance and several derived spectral indices. Spectra samples from ASTER Spectral Library Version 2.0 and Hyperion data archives were used in obtaining channel reflectance simulation of MSI and OLI. Preliminary results show that MSI and OLI are well comparable in several channels with small relative discrepancy (< 5 %), including the Costal Aerosol channel, a NIR (855–875 nm) channel, the SWIR channels, and the Cirrus channel. Meanwhile, for channels covering Blue, Green, Red, and NIR (785–900 nm), the between-sensor differences are significantly presented. Compared with the difference in reflectance of each individual channel, the difference in derived spectral index is more significant. In addition, effectiveness of linear transformation model is not ensured when the target belongs to another spectra collection. If an improper transformation model is selected, the between-sensor discrepancy will even largely increase. In conclusion, improvement in between-sensor consistency is possibly a challenge, through linear transformation based on model(s) generated from other spectra collections.