TY - GEN
T1 - Semantic supervised clustering approach to classify land cover in remotely sensed images
AU - Torres, Miguel
AU - Moreno, Marco
AU - Menchaca-Mendez, Rolando
AU - Quintero, Rolando
AU - Guzman, Giovanni
PY - 2010
Y1 - 2010
N2 - GIS applications involve applying classification algorithms to remotely sensed images to determine information about a specific region on the Earth's surface. These images are very useful sources of geographical data commonly used to classify land cover, analyze crop conditions, assess mineral and petroleum deposits and quantify urban growth. In this paper, we propose a semantic supervised clustering approach to classify multispectral information in satellite images. We use the maximum likelihood method to generate the clustering. In addition, we complement the analysis applying spatial semantics to determine the training sites and refine the classification. The approach considers the a priori knowledge of the remotely sensed images to define the classes related to the geographic environment. In this case, the properties and relations that involve the geo-image define the spatial semantics; these features are used to determine the training data sites. The method attempts to improve the supervised clustering, adding the intrinsic semantics of multispectral satellite images in order to establish the classes that involve the analysis with more precision.
AB - GIS applications involve applying classification algorithms to remotely sensed images to determine information about a specific region on the Earth's surface. These images are very useful sources of geographical data commonly used to classify land cover, analyze crop conditions, assess mineral and petroleum deposits and quantify urban growth. In this paper, we propose a semantic supervised clustering approach to classify multispectral information in satellite images. We use the maximum likelihood method to generate the clustering. In addition, we complement the analysis applying spatial semantics to determine the training sites and refine the classification. The approach considers the a priori knowledge of the remotely sensed images to define the classes related to the geographic environment. In this case, the properties and relations that involve the geo-image define the spatial semantics; these features are used to determine the training data sites. The method attempts to improve the supervised clustering, adding the intrinsic semantics of multispectral satellite images in order to establish the classes that involve the analysis with more precision.
UR - http://www.scopus.com/inward/record.url?scp=78650844031&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17641-8_10
DO - 10.1007/978-3-642-17641-8_10
M3 - Contribución a la conferencia
AN - SCOPUS:78650844031
SN - 3642176402
SN - 9783642176401
T3 - Communications in Computer and Information Science
SP - 68
EP - 77
BT - Signal Processing and Multimedia - International Conferences, SIP and MulGraB 2010, Held as Part of the Future Generation Information Technology Conference, FGIT 2010, Proceedings
T2 - 2010 International Conferences on Signal Processing, Image Processing and Pattern Recognition, SIP 2010 and Multimedia, Computer Graphics and Broadcasting, MulGraB 2010, Held as Part of FGIT 2010
Y2 - 13 December 2010 through 15 December 2010
ER -