6533b820fe1ef96bd1279c9f

RESEARCH PRODUCT

Use of Guided Regularized Random Forest for Biophysical Parameter Retrieval

Raul Zurita-millaEmma Izquierdo-v Erdiguler

subject

Mean squared error22/3 OA procedurebusiness.industryComputer scienceFeature extractionHyperspectral images0211 other engineering and technologiesHyperspectral imagingPattern recognitionFeature selection02 engineering and technologyBiophysical parameter retrievalRegularization (mathematics)RegressionRandom forestFeature selection0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligenceLeaf area indexbusinessRandom forest021101 geological & geomatics engineering

description

This paper introduces a feature selection method based on random forest -the Guided Regularized Random Forest (GRRF)- which can be used in classification and regression tasks. The method is based on the regularization of the information gain in the random forest nodes to obtain a subset of relevant and non-redundant features. The proposed method is used as a preliminary step In the process of retrieving biophysical parameters from a hyperspectral image. Preliminary experiments show that we can reduce the RMSE of the retrievals by around 7% for the Leaf Area Index and around 8% for the fraction of vegetation cover when compared to the results using random forest features.

yearjournalcountryeditionlanguage
2018-11-05IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
https://doi.org/10.1109/igarss.2018.8517920