The University of Southampton
University of Southampton Institutional Repository

Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture

Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture
Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture
A Random Forest (RF) classifier was applied to spectral as well as mono- and multi-seasonal textural features extracted from Landsat TM imagery to increase the accuracy of land cover classification over a complex Mediterranean landscape, with a large number of land cover categories and low inter-class separability. Five different types of geostatistical textural measure for three different window sizes and three different lags were applied creating a total of 972 potential input variables. Madograms, rodograms and direct variograms for the univariate case and cross- and pseudo-cross variograms for the multivariate case, together with multi-seasonal spectral information, were used in a RF classifier to map the landcover types. The pseudo-cross and cross variograms were used specifically to incorporate important seasonal/temporal information. Incorporating multi-scale textural features into the RF models led to an increase in the overall index of 10.71% and, for the most accurate classification, the increase was up to 30% in some classes. The differences in the kappa coefficient for the textural classification models were evaluated statistically using a pairwise Z-test, revealing a significant increase in per-class classification accuracy compared to GLCM-based texture measures. The pseudo-cross variogram between the visible and near-infrared bands was the most important textural features for general classification, and the multi-seasonal pseudo-cross variogram had an outstanding importance for agricultural classes. Overall, the RF classifier applied to a reduced subset of input variables composed of the most informative textural features led to the highest accuracy. Highly reliable classification results were obtained when the 16 most important textural features calculated at single scales (window sizes) were selected and used in the classification. The proposed methodology significantly increased the classification accuracy achieved with a spectral maximum likelihood classifier (ML). The kappa values for the textural RF and ML classifications were equal to 0.92 and 0.83, respectively.
texture, geostatistic, variogram, spatial autocorrelation, random forest
0034-4257
93-107
Rodriguez-Galiano, V.F.
1eb6a1dd-f73d-4e90-a9cf-a51f20712c3c
Chica-Olmo, M.
c7291c15-3b53-45d7-942c-06985f77d6f6
Abarca-Hernandez, F.
287e84cf-6fd0-4a81-ad6e-729318b7749b
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Jeganathan, C.
2dd3d151-7bb1-42b9-bda6-54df0bed4ae8
Rodriguez-Galiano, V.F.
1eb6a1dd-f73d-4e90-a9cf-a51f20712c3c
Chica-Olmo, M.
c7291c15-3b53-45d7-942c-06985f77d6f6
Abarca-Hernandez, F.
287e84cf-6fd0-4a81-ad6e-729318b7749b
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Jeganathan, C.
2dd3d151-7bb1-42b9-bda6-54df0bed4ae8

Rodriguez-Galiano, V.F., Chica-Olmo, M., Abarca-Hernandez, F., Atkinson, P.M. and Jeganathan, C. (2012) Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sensing of Environment, 121, 93-107. (doi:10.1016/j.rse.2011.12.003).

Record type: Article

Abstract

A Random Forest (RF) classifier was applied to spectral as well as mono- and multi-seasonal textural features extracted from Landsat TM imagery to increase the accuracy of land cover classification over a complex Mediterranean landscape, with a large number of land cover categories and low inter-class separability. Five different types of geostatistical textural measure for three different window sizes and three different lags were applied creating a total of 972 potential input variables. Madograms, rodograms and direct variograms for the univariate case and cross- and pseudo-cross variograms for the multivariate case, together with multi-seasonal spectral information, were used in a RF classifier to map the landcover types. The pseudo-cross and cross variograms were used specifically to incorporate important seasonal/temporal information. Incorporating multi-scale textural features into the RF models led to an increase in the overall index of 10.71% and, for the most accurate classification, the increase was up to 30% in some classes. The differences in the kappa coefficient for the textural classification models were evaluated statistically using a pairwise Z-test, revealing a significant increase in per-class classification accuracy compared to GLCM-based texture measures. The pseudo-cross variogram between the visible and near-infrared bands was the most important textural features for general classification, and the multi-seasonal pseudo-cross variogram had an outstanding importance for agricultural classes. Overall, the RF classifier applied to a reduced subset of input variables composed of the most informative textural features led to the highest accuracy. Highly reliable classification results were obtained when the 16 most important textural features calculated at single scales (window sizes) were selected and used in the classification. The proposed methodology significantly increased the classification accuracy achieved with a spectral maximum likelihood classifier (ML). The kappa values for the textural RF and ML classifications were equal to 0.92 and 0.83, respectively.

This record has no associated files available for download.

More information

e-pub ahead of print date: 17 February 2012
Published date: June 2012
Keywords: texture, geostatistic, variogram, spatial autocorrelation, random forest
Organisations: Global Env Change & Earth Observation

Identifiers

Local EPrints ID: 339910
URI: http://eprints.soton.ac.uk/id/eprint/339910
ISSN: 0034-4257
PURE UUID: a0e90d7f-d878-429a-8335-bbf7ec9516b3
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

Catalogue record

Date deposited: 01 Jun 2012 11:49
Last modified: 15 Mar 2024 02:47

Export record

Altmetrics

Contributors

Author: V.F. Rodriguez-Galiano
Author: M. Chica-Olmo
Author: F. Abarca-Hernandez
Author: P.M. Atkinson ORCID iD
Author: C. Jeganathan

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×