Automatic Correction of DEM Surface Errors

The automation of extracting planner surfaces is a main field of research in digital photogrammetry. These surfaces are essential to generate three dimensional GIS databases. Surfaces are usually determined from either DEMs or images. Each dataset provides a different type of information. Thus, the combination of the two datasets should enhance the surface reconstruction process. This paper presents a new technique for generating 3D surfaces by combining both correlation-based DEMs and aerial images. The process starts by discriminating DEM points that represent planner surfaces using local statistics of neighboring elevations and intensities and point elevations. A segmented orthophoto is then used to group these points into different regions. The elevations of the points in each region are fed into a least squares adjustment model to compute the best-fit planner surface parameters. Refinement of surface borders is then performed using a region growing algorithm. The RMSE for five test sites showed a spatial accuracy of 5 - 8 cm.

Fully automatic DOM generation method based on optical flow field dense image matching

Automatic Digital Orthophoto Map(DOM)generation plays an important role in many downstream works such as land use and cover detection,urban planning,and disaster assessment.Existing DOM generation methods can generate promising results but always need ground object filtered DEM generation before otho-rectification;this can consume much time and produce building facade contained results.To address this problem,a pixel-by-pixel digital differential rectification-based automatic DOM generation method is proposed in this paper.Firstly,3D point clouds with texture are generated by dense image matching based on an optical flow field for a stereo pair of images,respectively.Then,the grayscale of the digital differential rectification image is extracted directly from the point clouds element by element according to the nearest neighbor method for matched points.Subsequently,the elevation is repaired grid-by-grid using the multi-layer Locally Refined B-spline(LR-B)interpolation method with triangular mesh constraint for the point clouds void area,and the grayscale is obtained by the indirect scheme of digital differential rectification to generate the pixel-by-pixel digital differentially rectified image of a single image slice.Finally,a seamline network is automatically searched using a disparity map optimization algorithm,and DOM is smartly mosaicked.The qualitative and quantitative experimental results on three datasets were produced and evaluated,which confirmed the feasibility of the proposed method,and the DOM accuracy can reach 1 Ground Sample Distance(GSD)level.The comparison experiment with the state-of-the-art commercial softwares showed that the proposed method generated DOM has a better visual effect on building boundaries and roof completeness with comparable accuracy and computational efficiency.