Improved Contextual Classifiers of Multispectral Image Data
Summary :
Contextual classification of multispectral image data in remote sensing is discussed and concretely two improved contextual classifiers are proposed. The first is the extended adaptive classifier which partitions an image successively into homogeneously distributed square regions and applies a collective classification decision to each region. The second is the accelerated probabilistic relaxation which updates a classification result fast by adopting a pixelwise stopping rule. The evaluation experiment with a pseudo LANDSAT multispectral image shows that the proposed methods give higher classification accuracies than the compound decision method known as a standard contextual classifier.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E77-A No.9 pp.1445-1450
- Publication Date
- 1994/09/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section of Papers Selected from the 8th Digital Signal Processing Symposium)
- Category
- Image Processing
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Takashi WATANABE, Hitoshi SUZUKI, Sumio TANBA, Ryuzo YOKOYAMA, "Improved Contextual Classifiers of Multispectral Image Data" in IEICE TRANSACTIONS on Fundamentals,
vol. E77-A, no. 9, pp. 1445-1450, September 1994, doi: .
Abstract: Contextual classification of multispectral image data in remote sensing is discussed and concretely two improved contextual classifiers are proposed. The first is the extended adaptive classifier which partitions an image successively into homogeneously distributed square regions and applies a collective classification decision to each region. The second is the accelerated probabilistic relaxation which updates a classification result fast by adopting a pixelwise stopping rule. The evaluation experiment with a pseudo LANDSAT multispectral image shows that the proposed methods give higher classification accuracies than the compound decision method known as a standard contextual classifier.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e77-a_9_1445/_p
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@ARTICLE{e77-a_9_1445,
author={Takashi WATANABE, Hitoshi SUZUKI, Sumio TANBA, Ryuzo YOKOYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Improved Contextual Classifiers of Multispectral Image Data},
year={1994},
volume={E77-A},
number={9},
pages={1445-1450},
abstract={Contextual classification of multispectral image data in remote sensing is discussed and concretely two improved contextual classifiers are proposed. The first is the extended adaptive classifier which partitions an image successively into homogeneously distributed square regions and applies a collective classification decision to each region. The second is the accelerated probabilistic relaxation which updates a classification result fast by adopting a pixelwise stopping rule. The evaluation experiment with a pseudo LANDSAT multispectral image shows that the proposed methods give higher classification accuracies than the compound decision method known as a standard contextual classifier.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Improved Contextual Classifiers of Multispectral Image Data
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1445
EP - 1450
AU - Takashi WATANABE
AU - Hitoshi SUZUKI
AU - Sumio TANBA
AU - Ryuzo YOKOYAMA
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E77-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1994
AB - Contextual classification of multispectral image data in remote sensing is discussed and concretely two improved contextual classifiers are proposed. The first is the extended adaptive classifier which partitions an image successively into homogeneously distributed square regions and applies a collective classification decision to each region. The second is the accelerated probabilistic relaxation which updates a classification result fast by adopting a pixelwise stopping rule. The evaluation experiment with a pseudo LANDSAT multispectral image shows that the proposed methods give higher classification accuracies than the compound decision method known as a standard contextual classifier.
ER -
English
