DocumentCode :
2292546
Title :
Multimodality image registration and fusion using neural network
Author :
Mostafa, Mostafa G. ; Farag, Aly A. ; Essock, Edward
Author_Institution :
Dept. of Electr. & Comput. Eng., Louisville Univ., KY, USA
Volume :
2
fYear :
2000
fDate :
10-13 July 2000
Abstract :
Multi-modality image registration and fusion are essential steps in building 3D models from remote sensing data. In this paper, we present a neural network technique for the registration and fusion of multi-modality remote sensing data for the reconstruction of 3D models of terrain regions. A feedforward neural network is used to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data are presented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusion results.
Keywords :
feedforward neural nets; geometry; image reconstruction; image registration; interpolation; learning (artificial intelligence); performance evaluation; remote sensing; sensor fusion; visual perception; 3D model building; 3D model reconstruction; feedforward neural network; geometry learning; human performance evaluation; image interpolation; intensity data sets; multi-modality image fusion; multi-modality image registration; perceptual tests; remote sensing data; spatial data set; terrain regions; Geometry; Hyperspectral sensors; Image reconstruction; Image registration; Intelligent sensors; Laser radar; Neural networks; Remote monitoring; Remote sensing; Satellites;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion, 2000. FUSION 2000. Proceedings of the Third International Conference on
Conference_Location :
Paris, France
Print_ISBN :
2-7257-0000-0
Type :
conf
DOI :
10.1109/IFIC.2000.859857
Filename :
859857
Link To Document :
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