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Object-based classification of grasslands from high resolution satellite image time series using gaussian mean map kernels

Lopes, Maïlys and Fauvel, Mathieu and Girard, Stéphane and Sheeren, David Object-based classification of grasslands from high resolution satellite image time series using gaussian mean map kernels. (2017) Remote Sensing, 9 (7). 1-24. ISSN 2072-4292

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Official URL: http://dx.doi.org/10.3390/rs9070688

Abstract

This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the a-Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands’ management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands’ discrimination. Results are compared to other existing pixel- and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.

Item Type:Article
Additional Information:Thanks to MDPI editor. The definitive version is available at http://www.mdpi.com/ The original PDF of the article can be found at Remote Sensing website : http://www.mdpi.com/journal/remotesensing
HAL Id:hal-01636318
ProdINRA Id:397923
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Other partners > Institut polytechnique de Grenoble (FRANCE)
French research institutions > Institut National de la Recherche Agronomique - INRA (FRANCE)
French research institutions > Institut National de la Recherche en Informatique et en Automatique - INRIA (FRANCE)
Other partners > Université Pierre Mendès France, Grenoble 2 - UPMF (FRANCE)
Other partners > Université Joseph Fourier Grenoble 1 - UJF (FRANCE)
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Deposited By: INRA INRA
Deposited On:07 Nov 2017 15:04

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