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Title

EFFICIENCY AND CAPABILITY OF USING ARTIFICIAL INTELLIGENT ALGORITHMS FOR ORTHO-IMAGE AND DIGITAL ELEVATION MODEL GENERATION

Writers

BAGHERI H. | SADEGHIAN S.

Pages

 Start Page 98 | End Page 108

Abstract

 Nowadays the artificial intelligent algorithms has considered in GIS and remote sensing. GENETIC ALGORITHM and ARTIFICIAL NEURAL NETWORK are two intelligent methods that are used for optimizing of image processing programs such as edge extraction and etc. these algorithms are very useful for solving of complex program. In this paper, the ability and application of GENETIC ALGORITHM and ARTIFICIAL NEURAL NETWORK in geospatial production process like GEOMETRIC MODELING of satellite images for ortho photo generation and HEIGHT INTERPOLATION in raster Digital Terrain Model production process is discussed. In first, the geometric potential of Ikonos-2 and Worldview-2 with rational functions, 2D & 3D polynomials were tested. Also comprehensive experiments have been carried out to evaluate the viability of the GENETIC ALGORITHM for optimization of rational function, 2D & 3D polynomials. Considering the quality of Ground Control Points, the accuracy (RMSE) with GENETIC ALGORITHM and 3D polynomials method for Ikonos-2 Geo image was 0.508 pixel sizes and the accuracy (RMSE) with GA algorithm and rational function method for Worldview-2 image was 0.930 pixel sizes. For more another optimization artificial intelligent methods, neural networks were used. With the use of perception network in Worldview-2 image, a result of 0.84 pixel sizes with 4 neurons in middle layer was gained. The final conclusion was that with artificial intelligent algorithms it is possible to optimize the existing models and have better results than usual ones.Finally the artificial intelligence methods, like GENETIC ALGORITHMs as well as neural networks, were examined on sample data for optimizing interpolation and for generating Digital Terrain Models. The results then were compared with existing conventional methods and it appeared that these methods have a high capacity in heights interpolation and that using these networks for interpolating and optimizing the weighting methods based on inverse distance leads to a high accurate estimation of heights.

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