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Indexing of mid-resolution satellite images with structural attributes.

Bhattacharya, A. and Roux, M. and Maitre, H. and Jermyn, I.H. and Descombes, X. and Zerubia, J. (2008) 'Indexing of mid-resolution satellite images with structural attributes.', in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, part B4. Hannover: International Society for Photogrammetry and Remote Sensing, pp. 187-192.


Satellite image classification has been a major research field for many years with its varied applications in the field of Geography, Geology, Archaeology, Environmental Sciences and Military purposes. Many different techniques have been proposed to classify satellite images with color, shape and texture features. Complex indices like Vegetation index (NDVI), Brightness index (BI) or Urban index (ISU) are used for multi-spectral or hyper-spectral satellite images. In this paper we will show the efficiency of structural features describing man-made objects in mid-resolution satellite images to describe image content. We will then show the state-of-the-art to classify large satellite images with structural features computed from road networks and urban regions extracted on small image patches cut in the large image. Fisher Linear Discriminant (FLD) analysis is used for feature selection and a one-vsrest probabilistic Gaussian kernel Support Vector Machines (SVM) classification method is used to classify the images. The classification probabilities associated with each subimage of the large image provide an estimate of the geographical class coverage.

Item Type:Book chapter
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Available under License - Creative Commons Attribution.
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Publisher statement:Article published under a Creative Common Attribution 3.0 License.
Date accepted:No date available
Date deposited:19 April 2016
Date of first online publication:July 2008
Date first made open access:No date available

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