DocumentCode :
2089853
Title :
Spatial resolution improvement of spatial shift multi-observation images by neural network
Author :
Lu, Yao ; Inamura, Minoru
Author_Institution :
Dept. of Electron. Eng., Gunma Univ., Japan
Volume :
1
fYear :
2002
fDate :
24-28 June 2002
Firstpage :
92
Abstract :
In this paper, improvements on spatial resolution of the spatial shift multi-observation images are discussed. And a block of pixels-based artificial neural network is proposed for this purpose. This system makes full use of the spatial information to implement the superposition of multiple images. Its convergence and learning problems are also discussed. The effectiveness and the high performance of the proposed neural network are demonstrated by computer experiments, error calculation and comparison with other methods.
Keywords :
feedforward neural nets; geophysical signal processing; geophysical techniques; image processing; sensor fusion; terrain mapping; feedforward neural net; geophysical measurement technique; image processing; land surface; large pixel neural net; neural net; neural network; remote sensing; spatial resolution improvement; spatial shift multiobservation images; terrain mapping; Artificial neural networks; Computer networks; Convergence; High performance computing; Image resolution; Neural networks; Pixel; Quantum computing; Remote sensing; Spatial resolution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International
Print_ISBN :
0-7803-7536-X
Type :
conf
DOI :
10.1109/IGARSS.2002.1024951
Filename :
1024951
Link To Document :
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