Some experiments with spatial feature extraction methods in multispectral classification
作者:
LUCIANOV. DUTRA,
NELSOND. A. MASCARENHAS,
期刊:
International Journal of Remote Sensing
(Taylor Available online 1984)
卷期:
Volume 5,
issue 2
页码: 303-313
ISSN:0143-1161
年代: 1984
DOI:10.1080/01431168408948810
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Feature extraction is an important factor in determining the accuracy that can be attained in the classification of multispectral images. The traditional per point classification methods do not use all the available information, since they disregard the spatial relationships that exist among pixels belonging to the same class. In this paper, methods are developed to extract additional image spatial features by means of linear and non-linear local filtering. Feature selection methods are also developed, since it is usually not possible to use all the generated features. The classification stage is performed in a supervised mode using the maximum likelihood criterion. A quantitative analysis of the performance of the spatial features show that an overall increase in precision of classification is achieved, although at the expense of increased rejection levels, particularly on the borders between different fields.
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