In this paper, an approach for the similarity-based global optimization of buildings in urban scene is presented. In the
past, most researches concentrated on single building reconstruction, making it difficult to reconstruct reliable models
from noisy or incomplete point clouds. To obtain a better result, a new trend is to utilize the similarity among the
buildings. Therefore, a new similarity detection and global optimization strategy is adopted to modify local-fitting
geometric errors. Firstly, the hierarchical structure that consists of geometric, topological and semantic features is
constructed to represent complex roof models. Secondly, similar roof models can be detected by combining primitive
structure and connection similarities. At last, the global optimization strategy is applied to preserve the consistency and
precision of similar roof structures. Moreover, non-local consolidation is adapted to detect small roof parts. The
experiments reveal that the proposed method can obtain convincing roof models and promote the reconstruction quality
of 3D buildings in urban scene.
In this paper, we propose a registration method of high-resolution satellite images with geographic coordinates or
rational polynomial coefficients(RPC), which is the relationship between images and ground. Our approach consists of
two steps: firstly, a rough image registration is implemented on the basis of the "correction based on the projection"
theory that is an approximate epipolar line theory. Then, point features and line features in the image extracted by a
combination of corner extraction operators and line feature extraction algorithm will be the elements of the image
matching. A binding triangle net is constituted by all the features extracted to restrict the observed values. And in the end
of the process a high-precision automatic registration is performed by the least squares image matching method.
In this paper, a guide star selection algorithm based on angular grids is presented, which can be used to minimize the size
of initial star catalogue and guarantee the distribution of the guide stars as uniform as possible . This algorithm is
preformed by dividing the FOV into many equal angular grids and mapping the grids onto the celestial sphere. The guide
stars are selected in the extent of grids and their brightness and position in the celestial sphere are considered as well.
The experiment with real star catalogue data demonstrates the validity of the proposed algorithm.
The traditional orthophoto rectification often suffers from the problems of building lean and double mapping, etc., which are caused by no detecting the occluded areas, therefore, many improved occlusion detection methods had been discussed to solve these problems, such as angle and height based ray tracing method, angle-based method and Z-buffer method, etc. Angle and height based ray tracing method based on two different DSM models will be discussed in this paper, Experimental results demonstrate that the accuracy based on TIN DSM is better than that based on dense grid DSM.
Attitude estimation method is one of influencing factors for the attitude accuracy. Traditionally, the elements of the
rotation matrix as attitude unknowns are estimated optimally, but the solved attitude angles based on the elements of
rotation matrix aren't optimal. A rigorous attitude estimation approach for satellite attitude determination based on star
sensor is presented in this paper, which directly considers three-axis attitude angles as attitude unknowns. The
experiment indicates the proposed approach can improve the attitude accuracy to a great degree when the position errors
of image points are within ±0.5 pixel, and the efficiency can be guaranteed as well.
The low satellite attitude accuracy determined by star sensors is one of the key problems to high accuracy satellite data
acquired in China. Major error sources affecting the attitude accuracy are systematically analyzed, and the relationships
between these error sources and attitude accuracy are investigated qualitatively and quantitatively in the paper. The
regularity will be summarized, which can provide a helpful reference guide for improving the attitude accuracy. Some
methods and strategies to improve the attitude accuracy can be brought forwards and discussed based on the results of
Traditionally, the splitting and merging algorithm of image segmentation is based on quad tree data structure, which is not convenient to express the topography of regions, the line segments and other information. A new framework is discussed in this paper. It is "TIN based image segmentation and grouping", in which edge information and region information are integrated directly. Firstly, the constrained triangle mesh is constructed with edge segments extracted by EDISON or other algorithm. And then, region growing based on triangles is processed to generate a coarse segmentation. At last, the regions are combined further with perceptual organization rule.
Image Matching is the most important work in DPS(digital photogrammetry system). In the past, grid based image matching is adopted to generate DEM of regular grids in many DPS software. Great success has been achieved, while many problems exist. In this paper, a new matching scheme is put forward, in which image features such as points, lines(edges) and regions are organized in a constrained TIN on left image. For every vertex of TIN, possible matching candidates are searched. At last combination optimization is done to determine the true matched points for every vertex.