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Article

Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

1
Dynafor, University of Toulouse, INRA, INPT, INPT-EI PURPAN, 31326 Castanet Tolosan, France
2
Team Mistis, INRIA Rhône-Alpes, LJK, 38334 Montbonnot, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(7), 688; https://doi.org/10.3390/rs9070688
Received: 26 April 2017 / Revised: 12 June 2017 / Accepted: 29 June 2017 / Published: 4 July 2017
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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 α -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. View Full-Text
Keywords: supervised classification; SVM; Gaussian mean map kernels; kernel methods; object analysis; grasslands supervised classification; SVM; Gaussian mean map kernels; kernel methods; object analysis; grasslands
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MDPI and ACS Style

Lopes, M.; Fauvel, M.; Girard, S.; Sheeren, D. Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels. Remote Sens. 2017, 9, 688. https://doi.org/10.3390/rs9070688

AMA Style

Lopes M, Fauvel M, Girard S, Sheeren D. Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels. Remote Sensing. 2017; 9(7):688. https://doi.org/10.3390/rs9070688

Chicago/Turabian Style

Lopes, Mailys, Mathieu Fauvel, Stéphane Girard, and David Sheeren. 2017. "Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels" Remote Sensing 9, no. 7: 688. https://doi.org/10.3390/rs9070688

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