Publication:
Isolation forests to evaluate class separability and the representativeness of training and validation areas in land cover classification

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2019_AlonsoSarria_etal.pdf(35.98 MB)
Date
2019-12-13
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Authors
Alonso-Sarria, Francisco ; Valdivieso Ros, Carmen ; Gomariz Castillo, Francisco
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Publisher
MDPI
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GeografĂ­a
DOI
https://doi.org/10.3390/rs11243000
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info:eu-repo/semantics/article
Description
© 2019 by the authors. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/ This document is the Published Manuscript version of a Published Work that appeared in final form in Remote Sensing. To access the final edited and published work see https://doi.org/10.3390/rs11243000
Abstract
Supervised land cover classification from remote sensing imagery is based on gathering a set of training areas to characterise each of the classes and to train a predictive model that is then used to predict land cover in the rest of the image. This procedure relies mainly on the assumptions of statistical separability of the classes and the representativeness of the training areas. This paper uses isolation forests, a type of random tree ensembles, to analyse both assumptions and to easily correct lack of representativeness by digitising new training areas where needed to improve the classification of a Landsat-8 set of images with Random Forest. The results show that the improved set of training areas after the isolation forest analysis is more representative of the whole image and increases classification accuracy. Besides, the distribution of isolation values can be useful to estimate class separability. A class separability parameter that summarises such distributions is proposed. This parameter is more correlated to omission and commission errors than other separability measures such as the Jeffries–Matusita distance.
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Training area representativeness , Class separability , Landsat-8 , Random tree ensembles , Random forest
Citation
Remote Sensing, 2019, Vol. 11 (24) : 3000
URI
http://hdl.handle.net/10201/147874
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