An algorithm for satellite tracking and orbit prediction is presented in detail. Firstly, the satellite's position and velocity
are calculated by the Simplified General Perturbations Version 4 and Simplified Deep-space Perturbation Version 4
(SGP4/SDP4) orbit propagation algorithms. And we put forward "Two-Point-Pair" which means two points that the
CCD of satellite's IFOV rays intersecting with the earth. We make use of the "Two-Point-Pair" to calculate the accuracy
bounding box of satellite at the instantaneous time through the satellite's position and velocity above. Besides, we build a
system called GeoGlobe to simulate the algorithm. The system can be used to simulate all kinds of satellites that the
Two-Line Element (TLE) Sets files can provide.
A simulation system is developed for satellite tracking and orbit prediction. The program displays the location of Earth
satellites and predicts the location of any satellite at any time. And the simulation of satellite tracking and orbit
prediction take the swaths into consideration. The orbit prediction model and Two-Line Element (TLE) Sets are present
first, and the Simplified General Perturbations Version 4 and Simplified Deep-space Perturbation Version 4
(SGP4/SDP4) orbit propagation algorithms are introduced then. In the end, we introduce how we build our system in
detail. In the end we present the comparison of the prediction result between the system of ours and STK's. The result
shows that SGP4/SDP4 are efficient and valid in our system.
Stereo matching is one of the most important and challenging subjects in computer vision, digital photogrammetry, and
image understanding. For the purpose of wide-baseline stereo matching, a novel approach on high-quality affine
invariant feature extraction is proposed. The key contribution of the novel approach is a filtering strategy for affine
invariant features detecting based on information content and spatial dispersion quality constraints. The essential idea is
to remove the features with low information content and bad distribution, just select the high-quality features (high
information content and good distribution). Based on the filtering strategy, an automatic algorithm on high-quality affine
invariant feature extraction is introduced. The experiment using image sequences with different texture conditions proves
that our algorithm can get much higher repeatability than the other algorithms, which is more suitable for subsequent
wide baseline stereo matching.
KEYWORDS: Data modeling, Buildings, Clouds, Visualization, Databases, Geographic information systems, Computing systems, Systems modeling, 3D visualizations, Binary data
A global data model is presented for the digital earth that provides fast access. It is shown how the same global
hierarchical structure can be used for a wide variety of geospatial data and how to organize the specific geospatial data.
In order to maximize efficiency, particular caching and paging mechanism have been used. Some types of data can, at
prescribed levels of detail, transition from the global model to type-specific data structures and detail management
schemes will be introduced. This framework has been applied successfully to a variety of data including terrain
elevations, imagery, maps, buildings, clouds, and other data. Further, the framework provides a visual navigation
approach where one can navigate continuously from global overviews to high resolution local views. This paper presents
results for several applications.
Networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. Recently Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. However, in the classification domain it was not paid attention to by researchers until the simplest form of Bayesian Networks, Naive Bayesian Network, turned up. In this paper, Naive Bayesian Network is applied to texture classification of aerial image. In order to validate the utility of Naive Bayesian Classifier, six hundred and eighty-four aerial images are used in the experiment and results demonstrate Naive Bayesian Classifier needs less computational costs than maximum likelihood method during classification and outperforms maximum likelihood method in the classification accuracy. Therefore, it is an attractive and effective method, and it will lead to its wide application.
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