Automatic Classification Method for Multitemporal Data using Reference Map

A new automatic classification method with high and stable accuracy for multitemporal data is presented in this paper. This method is based on prior condition that a classified map of the target area already exists, or at least one of the multitemporal data had been classified. The classified map is...

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Published inJournal of the Japan society of photogrammetry and remote sensing Vol. 31; no. 3; pp. 25 - 33
Main Authors HONG, Sunpyo, FUKUE, Kiyonari, HASHINO, Tsukasa, SHIMODA, Haruhisa, SAKATA, Toshibumi
Format Journal Article
LanguageEnglish
Published Japan Society of Photogrammetry and Remote Sensing 1992
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Abstract A new automatic classification method with high and stable accuracy for multitemporal data is presented in this paper. This method is based on prior condition that a classified map of the target area already exists, or at least one of the multitemporal data had been classified. The classified map is used as a reference map to specify training areas of classification categories. The new automatic classification method consists of five steps, i. e., extraction of training data using the reference map, detection of changed pixels based upon the homogeneity of training data, clustering of changed pixels, reconstruction of training data, and maximum likelihood classification. In order to evaluate the performance of this method, each temporal Landsat TM data were classified by using this method and a conventional method. As a result, we could get classified maps with high reliability and fast throughput, without a skilled operator.
AbstractList A new automatic classification method with high and stable accuracy for multitemporal data is presented in this paper. This method is based on prior condition that a classified map of the target area already exists, or at least one of the multitemporal data had been classified. The classified map is used as a reference map to specify training areas of classification categories. The new automatic classification method consists of five steps, i. e., extraction of training data using the reference map, detection of changed pixels based upon the homogeneity of training data, clustering of changed pixels, reconstruction of training data, and maximum likelihood classification. In order to evaluate the performance of this method, each temporal Landsat TM data were classified by using this method and a conventional method. As a result, we could get classified maps with high reliability and fast throughput, without a skilled operator.
Author HONG, Sunpyo
HASHINO, Tsukasa
SHIMODA, Haruhisa
FUKUE, Kiyonari
SAKATA, Toshibumi
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  fullname: SHIMODA, Haruhisa
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  fullname: SAKATA, Toshibumi
  organization: Tokai University Research & Information Center
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