The satellite, Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) has been equipped with a new type of spaceborne laser altimeter, which has the benefits of having small footprints and a high repetition rate, and it can produce dense footprints on the ground. Focusing on the pointing angle calibration of this new spaceborne laser altimeter, this paper proposes a fast pointing angle calibration method using only a small range of terrain surveyed by airborne lidar. Based on the matching criterion of least elevation difference, an iterative pointing angle calibration method was proposed. In the experiment, the simulated photon-counting laser altimeter data and the Ice, Cloud and Land Elevation Satellite-2 data were used to verify the algorithm. The results show that when 1 km and 2.5 km lengths of track were used, the pointing angle error after calibration could be reduced to about 0.3 arc-seconds and less than 0.1 arc-seconds, respectively. Meanwhile, compared with the traditional pyramid search method, the proposed iterative pointing angle calibration method does not require well-designed parameters, which are important in the pyramid search method to balance calculation time and calibration result, and the iterative pointing angle calibration method could significantly reduce the calibration time to only about one-fifth of that of the pyramid search method.

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Iterative Pointing Angle Calibration Method for the Spaceborne Photon-Counting Laser Altimeter Based on Small-Range Terrain Matching

Rapidly growing cities, multiple uses of urban space and the complexity of overlapping property rights require various types of rights to be registered and handled in a uniform and reliable way, considering the third dimension. The adoption of automated and low-cost but reliable procedures for cadastral surveys and for the capture and processing of cadastral data, as well as the use of modern Information Technology (IT) tools and m-services, is the beginning of a new cadastral evolution. 3D-crowdsourced cadastral data capture has huge potential and may soon facilitate the work of National Mapping Agencies (NMAs). In this paper, an innovative fit-for-purpose procedure is designed and initially tested that aims to save time and costs and to provide a modern technical solution for the initial collection, registration and visualization of 3D cadastral data. An open-source, mobile application for the acquisition of 3D crowdsourced cadastral data and 3D modelling and visualization of property units is developed, tested and presented. The proposed technical procedure is adjustable and may be used in both the developed and the developing world. The geometric accuracy of the final product depends on the geometric accuracy of the basemaps used. The developed application is tested on a multi-story building in an urban area of Larisa, in Greece. An initial evaluation of the procedure and the final product, in terms of its usability, affordability, reliability and implementation duration, is conducted. The first results are satisfactory and may lead to a fit-for-purpose procedure for a 3D cadastre for all in the future.

Crowdsourced 3D cadastral surveys: looking towards the next 10 years

The high-resolution satellite imageries (HRSI) are as primary dataset for different applications such as DEM generation, 3D city mapping, change detection, monitoring, and deformation detection. The geo-location information of HRSI are stored in metadata called Rational Polynomial Coefficients (RPCs). There are many methods to improve and modify the RPCs in order to have a precise mapping. In this paper, an automatic approach is presented for the RPC modification using global Digital Elevation Models. The main steps of this approach are: relative digital elevation model generation, shift parameters calculation, sparse point cloud generation and shift correction, and rational polynomial fitting. Using some ground control points, the accuracy of the proposed method is evaluated based on statistical descriptors in which the results show that the geo-location accuracy of HRSI can be improved without using Ground Control Points (GCPs).

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The rational polynomial coefficients modification using digital elevation models

 After GLAS (Geo-science Laser Altimeter System) loaded on the ICESat (Ice Cloud and land Elevation Satellite), satellite laser altimeter attracts more and more attention ZY3-02 equipped with the Chinese first satellite laser altimeter has been successfully launched on 30th May, 2016 The geometric calibration is an important step for the laser data processing and application The method to calculate the laser pointing angle error based on existed reference terrain data is proposed in this paper The public version terrain data, such as 90m-SRTM and 30m-AW3D30, can be used to estimate the pointing angle of laser altimeter The GLAS data with simulated pointing error and actual ZY3-02 laser altimetry data is experimented to validate the algorithm The conclusion will be useful for the future domestic satellite laser altimeter

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Pointing angle calibration of zy3-02 satellite laser altimeter using terrain matching

Georeferencing satellite imagery is the basis for mapping and other subsequent processing in photogrammetry and remote sensing. Traditional georeferencing methods, which require adequate and well-distributed ground control points (GCPs), are time-consuming, costly, constantly cumbersome and inefficient. This study proposes a method of adaptive re-weighted block adjustment without GCPs for satellite stereo images. Furthermore, an approach of using multi-coverage images to improve the direct georeferencing accuracy is also investigated. The bias-compensated rational function model with affine transformation parameters is used as the adjustment mathematical model. The “weighted virtual control points” are introduced as constraints in the adjustment. Space intersection and resection are executed iteratively to solve the accurate affine transformation parameters. A total of 722 three-line array stereo scenes of Chinese Mapping Satellite-1 images captured from December 2010 to March 2016, which cover the entire Shandong Province of China (about 158,000 km2) nearly 10 times in space, are used as experimental data to evaluate the performance of the proposed method. Experiment results demonstrate that the proposed method can effectively eliminate the systematic errors of images. Moreover, the additional comparison with experiments of three groups of mono-coverage images indicates that the redundant observations from multi-coverage images can significantly improve the direct georeferencing accuracy, more specifically, from 13.69–16.02 m to 11.09 m in the horizontal direction, and from 16.39–29.27 m to 8.47 m in the vertical direction.

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Adaptive Re-weighted Block Adjustment for Multi-Coverage Satellite Stereo Images without Ground Control Points

For precise mapping using high resolution satellite images, various ways to eliminate the need of ground control points have been proposed. DEM matching, a technique to match two different surfaces by adjusting their position and attitude, is also one way of them. Our previous study demonstrated that DEM matching can be used for bias compensation of rigorous pushbroom sensor models. In this paper, we exploit the DEM matching technique further using existing global elevation dataset for absolute orientation of high resolution satellite image without ground control points. In particular, we investigated how the existing global DEM available worldwide can substitute ground control points when DEM matching is applied to linear pushbroom imagery. We used Spot-5 HRG with 2.5m ground sampling distance(GSD) as high resolution satellite images and used SRTM-derived DEMs(DTEDs) with gird spacing of 30m and 90m as existing elevation dataset. We firstly established relative orientation and generated a relative DEM without use of ground control points. DEM matching was then applied between the relative DEM and the DTED. We applied DEM matching based on linear pushbroom images. Through DEM matching, accuracy of about 2 GSD or better could be achieved with in the horizontal and in the vertical direction, respectively. We confirmed automatic DEM matching processing can be accomplished for absolute orientation of pushbroom image and as results, absolute DEM can be achieved from relative DEM. We tested the applicability of 90m existing global dataset for DEM matching. Our experimental results showed the potential of existing DEM for precise mapping without ground control points. This is very encouraging in terms of applicability of the existing global elevation data. This paper will contribute to understanding of DEM matching for linear pushbroom images and of solution to precise mapping without ground control points.

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The use of existing global elevation dataset for absolute orientation of high resolution image without gcps

Research on 3D urban modelling has been actively carried out for a long time. Recently the need of 3D urban modelling research is increased rapidly due to improved geo-web services and popularized smart devices. Nowadays 3D urban models provided by, for example, Google Earth use aerial photos for 3D urban modelling but there are some limitations: immediate update for the change of building models is difficult, many buildings are without 3D model and texture, and large resources for maintaining and updating are inevitable. To resolve the limitations mentioned above, we propose a method for semi-automatic building modelling and facade texture mapping from mobile phone images and analyze the result of modelling with actual measurements. Our method consists of camera geometry estimation step, image matching step, and facade mapping step. Models generated from this method were compared with actual measurement value of real buildings. Ratios of edge length of models and measurements were compared. Result showed 5.8% average error of length ratio. Through this method, we could generate a simple building model with fine facade textures without expensive dedicated tools and dataset.

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J. Jeong

Papers

The use of existing global elevation dataset for absolute orientation of high resolution image without gcps

Semi-automatic building models and façade texture mapping from mobile phone images