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A variational approach to intensity approximation for remote sensing images using dynamic neural networks.
- Source :
-
Expert Systems . Sep2003, Vol. 20 Issue 4, p163-170. 8p. - Publication Year :
- 2003
-
Abstract
- In remote sensing image processing, image approximation, or obtaining a high-resolution image from a corresponding low-resolution image, is an ill-posed inverse problem. In this paper, the regularization method is used to convert the image approximation problem into a solvable variational problem. In regularization, the constraints on smoothness and discontinuity are considered, and the original ill-posed problem is thereby converted to a well-posed optimization problem. In order to solve the variational problem, a Hopfield-type dynamic neural network is developed. This neural network possesses two states that describe the discrepancy between a pixel and adjacent pixels, the intensity evolution of a pixel and two kinds of corresponding weights. Based on the experiment in this study with a Landsat TM image free of added noise and a noisy image, the proposed approach provides better results than other methods. The comparison shows the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02664720
- Volume :
- 20
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Expert Systems
- Publication Type :
- Academic Journal
- Accession number :
- 10894832
- Full Text :
- https://doi.org/10.1111/1468-0394.00240