IEICE TRANSACTIONS on Information
Knowledge-Based Enhancement of Low Spatial Resolution Images
Summary :
Many image-processing techniques are based on texture features or gradation features of the image. However, Landsat images are complex; they also include physical features of reflection radiation and heat radiation from land cover. In this paper, we describe a method of constructing a super-resolution image of Band 6 of the Landsat TM sensor, oriented to analysis of an agricultural area, by combining information (texture features, gradation features, physical features) from other bands. In this method, a knowledge-based hierarchical classifier is first used to identify land cover in each pixel and then the least-squares approach is applied to estimate the mean temperature of each type of land cover. By reassigning the mean temperature to each pixel, a finer spatial resolution is obtained in Band 6. Computational results show the efficiency of this method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E81-D No.5 pp.457-463
- Publication Date
- 1998/05/25
- Publicized
- Online ISSN
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- Type of Manuscript
- Category
- Image Processing,Computer Graphics and Pattern Recognition
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Xiao-Zheng LI, Mineichi KUDO, Jun TOYAMA, Masaru SHIMBO, "Knowledge-Based Enhancement of Low Spatial Resolution Images" in IEICE TRANSACTIONS on Information,
vol. E81-D, no. 5, pp. 457-463, May 1998, doi: .
Abstract: Many image-processing techniques are based on texture features or gradation features of the image. However, Landsat images are complex; they also include physical features of reflection radiation and heat radiation from land cover. In this paper, we describe a method of constructing a super-resolution image of Band 6 of the Landsat TM sensor, oriented to analysis of an agricultural area, by combining information (texture features, gradation features, physical features) from other bands. In this method, a knowledge-based hierarchical classifier is first used to identify land cover in each pixel and then the least-squares approach is applied to estimate the mean temperature of each type of land cover. By reassigning the mean temperature to each pixel, a finer spatial resolution is obtained in Band 6. Computational results show the efficiency of this method.
URL: https://global.ieice.org/en_transactions/information/10.1587/e81-d_5_457/_p
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@ARTICLE{e81-d_5_457,
author={Xiao-Zheng LI, Mineichi KUDO, Jun TOYAMA, Masaru SHIMBO, },
journal={IEICE TRANSACTIONS on Information},
title={Knowledge-Based Enhancement of Low Spatial Resolution Images},
year={1998},
volume={E81-D},
number={5},
pages={457-463},
abstract={Many image-processing techniques are based on texture features or gradation features of the image. However, Landsat images are complex; they also include physical features of reflection radiation and heat radiation from land cover. In this paper, we describe a method of constructing a super-resolution image of Band 6 of the Landsat TM sensor, oriented to analysis of an agricultural area, by combining information (texture features, gradation features, physical features) from other bands. In this method, a knowledge-based hierarchical classifier is first used to identify land cover in each pixel and then the least-squares approach is applied to estimate the mean temperature of each type of land cover. By reassigning the mean temperature to each pixel, a finer spatial resolution is obtained in Band 6. Computational results show the efficiency of this method.},
keywords={},
doi={},
ISSN={},
month={May},}
Copy
TY - JOUR
TI - Knowledge-Based Enhancement of Low Spatial Resolution Images
T2 - IEICE TRANSACTIONS on Information
SP - 457
EP - 463
AU - Xiao-Zheng LI
AU - Mineichi KUDO
AU - Jun TOYAMA
AU - Masaru SHIMBO
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E81-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 1998
AB - Many image-processing techniques are based on texture features or gradation features of the image. However, Landsat images are complex; they also include physical features of reflection radiation and heat radiation from land cover. In this paper, we describe a method of constructing a super-resolution image of Band 6 of the Landsat TM sensor, oriented to analysis of an agricultural area, by combining information (texture features, gradation features, physical features) from other bands. In this method, a knowledge-based hierarchical classifier is first used to identify land cover in each pixel and then the least-squares approach is applied to estimate the mean temperature of each type of land cover. By reassigning the mean temperature to each pixel, a finer spatial resolution is obtained in Band 6. Computational results show the efficiency of this method.
ER -
English
