Integrating a scale-invariant feature of fractal geometry into the Hopfield neural network for super-resolution mapping

Super-resolution mapping (SRM) is a potential technique to improve image pattern recognition by predicting the spatial distribution of class composition at a sub-pixel scale. A number of SRM techniques have been reported in the past two decades. Most of the techniques are based on the assumption of...

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Bibliographic Details
Published in:International journal of remote sensing 2019-12, Vol.40 (23), p.8933-8954
Main Author: Su, Yuan-Fong
Format: Article
Language:English
Subjects:
Algorithms
Dependence
Fractal geometry
Geographical distribution
Geometry
Invariants
Mapping
Neural networks
Object recognition
Pattern recognition
Resolution
Satellite imagery
Self-similarity
Spaceborne remote sensing
Spatial distribution
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Summary:Super-resolution mapping (SRM) is a potential technique to improve image pattern recognition by predicting the spatial distribution of class composition at a sub-pixel scale. A number of SRM techniques have been reported in the past two decades. Most of the techniques are based on the assumption of spatial dependence. In this paper, a scale-invariant concept of fractal geometry is taking into account in the original Hopfield neural network (HNN) algorithm and a self-similar Hopfield neural network (SSHNN) is proposed which based on both spatial dependence and self-similarity in the fractal geometry. Both synthetic and real satellite images are used to test the performance of the SSHNN. The results show that by taking self-similarity into consideration, with a single image and no other additional data needed, the mapping accuracy of the SSHNN increases by up to 20% compared to the HNN.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2019.1624865