The Directional Neighborhoods Approach to Contextual Classification of Images from Noisy Data
作者:
S.James Press,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1996)
卷期:
Volume 91,
issue 435
页码: 1091-1100
ISSN:0162-1459
年代: 1996
DOI:10.1080/01621459.1996.10476979
出版商: Taylor & Francis Group
关键词: Bayes;Pixels;Reconstruction;Restoration;Spatial
数据来源: Taylor
摘要:
The directional neighborhoods approach (DNA) to classifying pixels and reconstructing images from remotely sensed noisy data is a newly proposed computer-intensive procedure that is partly Bayesian and partly data analytic. It uses the observational data to select an optimal, generally asymmetric, but relatively homogeneous neighborhood for contextually classifying pixels. A criterion for “homogeneity of neighborhood” is developed. DNA involves two stages: a zero-neighbor preclassification stage, followed by selection of the most homogeneous neighborhood, and then a final classification. We provide Monte Carlo simulations for a two-population image and compare DNA results with those from a reference Bayesian contextual classification. We show that DNA improves substantially on the reference classification procedure.
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