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Incorporating the downscaled landsat TM thermal band in land-cover classification using random forest

Incorporating the downscaled landsat TM thermal band in land-cover classification using random forest
Incorporating the downscaled landsat TM thermal band in land-cover classification using random forest
Thermal information is a key parameter in numerous remote sensing applications and environmental studies. The aim of this study was to assess the improvement that incorporating the TIR band of the Landsat-5 TM sensor has in the classification of a large heterogeneous landscape located in the south of Spain. To incorporate the thermal data into the classification process, the TIR band (with spatial resolution of 120 m) was downscaled by means of a geostatistical method (Downscaling Cokriging) to achieve a spatial resolution of 30 meters. Then, the thermal information was evaluated for contribution to overall and per-class map accuracy using Random Forest classification. The addition of the TIR band to single-season and multi-seasonal Random Forest models leads to an increase in the overall accuracy of 10 percent and 5 percent, and to an increase in the kappa index of 10 percent and 5 percent, respectively. The increase in per-class kappa for the thermal, single-season, Random Forest model ranged from ?3 percent to 47 percent and 0 percent to 12 percent for the thermal, multi-seasonal model.
0099-1112
129-137
Rodriguez-Galiano, Victor
be6ea36a-6613-41ba-a4da-9e7b565d8d0d
Ghimire, Bardan
cfcb397b-39ac-41d2-adef-2469809e547f
Pardo-Iguzquiza, Eulogio
0ef5d070-ab54-4d5e-8e8b-6268ba360e92
Chica-Olmo, Mario
ff64f393-e295-440b-8d31-ca6964ee7c33
Congalton, Russell
ae17f643-6867-49e5-b032-ddf260802c08
Rodriguez-Galiano, Victor
be6ea36a-6613-41ba-a4da-9e7b565d8d0d
Ghimire, Bardan
cfcb397b-39ac-41d2-adef-2469809e547f
Pardo-Iguzquiza, Eulogio
0ef5d070-ab54-4d5e-8e8b-6268ba360e92
Chica-Olmo, Mario
ff64f393-e295-440b-8d31-ca6964ee7c33
Congalton, Russell
ae17f643-6867-49e5-b032-ddf260802c08

Rodriguez-Galiano, Victor, Ghimire, Bardan, Pardo-Iguzquiza, Eulogio, Chica-Olmo, Mario and Congalton, Russell (2012) Incorporating the downscaled landsat TM thermal band in land-cover classification using random forest. Photogrammetric Engineering and Remote Sensing, 78 (2), 129-137.

Record type: Article

Abstract

Thermal information is a key parameter in numerous remote sensing applications and environmental studies. The aim of this study was to assess the improvement that incorporating the TIR band of the Landsat-5 TM sensor has in the classification of a large heterogeneous landscape located in the south of Spain. To incorporate the thermal data into the classification process, the TIR band (with spatial resolution of 120 m) was downscaled by means of a geostatistical method (Downscaling Cokriging) to achieve a spatial resolution of 30 meters. Then, the thermal information was evaluated for contribution to overall and per-class map accuracy using Random Forest classification. The addition of the TIR band to single-season and multi-seasonal Random Forest models leads to an increase in the overall accuracy of 10 percent and 5 percent, and to an increase in the kappa index of 10 percent and 5 percent, respectively. The increase in per-class kappa for the thermal, single-season, Random Forest model ranged from ?3 percent to 47 percent and 0 percent to 12 percent for the thermal, multi-seasonal model.

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More information

Published date: February 2012
Organisations: Global Env Change & Earth Observation

Identifiers

Local EPrints ID: 360093
URI: http://eprints.soton.ac.uk/id/eprint/360093
ISSN: 0099-1112
PURE UUID: 294ae8b3-17ab-4d89-9acc-817ad6877af2

Catalogue record

Date deposited: 25 Nov 2013 14:00
Last modified: 22 Jul 2022 18:51

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Contributors

Author: Victor Rodriguez-Galiano
Author: Bardan Ghimire
Author: Eulogio Pardo-Iguzquiza
Author: Mario Chica-Olmo
Author: Russell Congalton

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