Next Article in Journal
Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment
Previous Article in Journal
Ten-Meter Sentinel-2A Cloud-Free Composite—Southern Africa 2016
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(7), 688; doi:10.3390/rs9070688

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.
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)
View Full-Text   |   Download PDF [5515 KB, uploaded 5 July 2017]   |  

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 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
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top