Remote sensing satellites play an increasingly prominent role in environmental monitoring and disaster rescue. Taking advantage of almost the same sunshine condition to same place and global coverage, most of these satellites are operated on the sun-synchronous orbit. However, it brings some problems inevitably, the most significant one is that the temporal resolution of sun-synchronous orbit satellite can’t satisfy the demand of specific region monitoring mission. To overcome the disadvantages, two methods are exploited: the first one is to build satellite constellation which contains multiple sunsynchronous satellites, just like the CHARTER mechanism has done; the second is to design non-predetermined orbit based on the concrete mission demand. An effective method for remote sensing satellite orbit design based on multiobjective evolution algorithm is presented in this paper. Orbit design problem is converted into a multi-objective optimization problem, and a fast and elitist multi-objective genetic algorithm is utilized to solve this problem. Firstly, the demand of the mission is transformed into multiple objective functions, and the six orbit elements of the satellite are taken as genes in design space, then a simulate evolution process is performed. An optimal resolution can be obtained after specified generation via evolution operation (selection, crossover, and mutation). To examine validity of the proposed method, a case study is introduced: Orbit design of an optical satellite for regional disaster monitoring, the mission demand include both minimizing the average revisit time internal of two objectives. The simulation result shows that the solution for this mission obtained by our method meet the demand the users’ demand. We can draw a conclusion that the method presented in this paper is efficient for remote sensing orbit design.
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.
KEYWORDS: LIDAR, Vegetation, Clouds, 3D modeling, 3D metrology, Remote sensing, 3D acquisition, Global Positioning System, 3D displays, Associative arrays
This paper discusses how to separate non-ground points from raw LIDAR point cloud. For the purpose of improving
processing efficiency and precision, an improved 1-D filtering method is proposed. The entire filtering process is divided
into eight steps and non-ground points are eliminated progressively. In these processing steps, a key-point detection
technique is used to segment points in profile. Based on these profile segments, detailed analysis is utilized to implement
segment-oriented filtering innovatively. This method makes use of entire features of segmental points for classification,
so it is more accuracy and robust than traditional point-by-point classification. Two different scale datasets are used to
test our method. Compared to 1-D labeling method, the proposed method is more effective and efficiency.
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