Cognitive approaches and optical multispectral data for semi-automated classification of landforms in a rugged mountainous area

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Camargo, F. F.; Almeida, C. M.; Costa, G. A. O. P.; Feitosa, R. Q.; Oliveira, D. A. B. et al.: Cognitive approaches and optical multispectral data for semi-automated classification of landforms in a rugged mountainous area. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: [Geobia 2010: Geographic Object-Based Image Analysis] 38-4 (2010), Nr. C7

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This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called InterIMAGE. This framework owns a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of the semantic network, which constitutes a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in InterIMAGE is presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic and drove the interpretation semantic network. Object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The InterIMAGE interpretation strategy yielded a classification result with strong agreement and proved to be effective for the extraction of landforms.
License of this version: CC BY 3.0
Document Type: article
Publishing status: publishedVersion
Issue Date: 2010
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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4 image of flag of Mexico Mexico 1 2.00%
5 image of flag of India India 1 2.00%

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