Semantic supervised clustering To land Classification In geo-images

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a semantic supervised clustering approach to classify lands in geo-images. We use the Maximum Likelihood Method to generate the clustering. In addition, we complement the analysis applying spatial semantics to improve the classification. The approach considers the a priori knowledge of the multispectral image to define the training sites (classes) related to the geographic environment. In this case the spatial semantics is defined by the spatial properties, functions and relations that involve the geo-image. By using these characteristics, it is possible to determine the training data sites with a priori knowledge. This method attempts to improve the supervised clustering, adding the intrinsic semantics of the geo-images to determine the training sites that involve the analysis with more precision. © Springer-Verlag Berlin Heidelberg 2005.
Original languageAmerican English
Title of host publicationSemantic supervised clustering To land Classification In geo-images
Pages248-254
Number of pages222
ISBN (Electronic)3540288961, 9783540288961
StatePublished - 1 Dec 2005
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2014 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3683 LNAI
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/14 → …

Fingerprint

Semantics
Clustering
Multispectral Images
Spatial Analysis
Maximum Likelihood Method
Maximum likelihood
Complement
Classify
Training
Knowledge

Cite this

Torres, M., Guzman, G., Quintero, R., Moreno, M., & Levachkine, S. (2005). Semantic supervised clustering To land Classification In geo-images. In Semantic supervised clustering To land Classification In geo-images (pp. 248-254). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).
Torres, Miguel ; Guzman, G. ; Quintero, Rolando ; Moreno, Marco ; Levachkine, Serguei. / Semantic supervised clustering To land Classification In geo-images. Semantic supervised clustering To land Classification In geo-images. 2005. pp. 248-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "In this paper, we propose a semantic supervised clustering approach to classify lands in geo-images. We use the Maximum Likelihood Method to generate the clustering. In addition, we complement the analysis applying spatial semantics to improve the classification. The approach considers the a priori knowledge of the multispectral image to define the training sites (classes) related to the geographic environment. In this case the spatial semantics is defined by the spatial properties, functions and relations that involve the geo-image. By using these characteristics, it is possible to determine the training data sites with a priori knowledge. This method attempts to improve the supervised clustering, adding the intrinsic semantics of the geo-images to determine the training sites that involve the analysis with more precision. {\circledC} Springer-Verlag Berlin Heidelberg 2005.",
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Torres, M, Guzman, G, Quintero, R, Moreno, M & Levachkine, S 2005, Semantic supervised clustering To land Classification In geo-images. in Semantic supervised clustering To land Classification In geo-images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3683 LNAI, pp. 248-254, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1/01/14.

Semantic supervised clustering To land Classification In geo-images. / Torres, Miguel; Guzman, G.; Quintero, Rolando; Moreno, Marco; Levachkine, Serguei.

Semantic supervised clustering To land Classification In geo-images. 2005. p. 248-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Torres M, Guzman G, Quintero R, Moreno M, Levachkine S. Semantic supervised clustering To land Classification In geo-images. In Semantic supervised clustering To land Classification In geo-images. 2005. p. 248-254. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).