Use of guided regularized random forest for biophysical parameter retrieval

E. Izquierdo-Verdiguier, R. Zurita-Milla

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Subtitle of host publicationProceedings
PublisherIEEE
Pages5776-5779
Number of pages4
ISBN (Electronic)9781538671504
DOIs
Publication statusPublished - 5 Nov 2018
Event38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018: Observing, Understanding and Forcasting the Dynamics of Our Planet - Feria Valencia Convention & Exhibition Center, Valencia, Spain
Duration: 22 Jul 201827 Jul 2018
Conference number: 38
https://www.igarss2018.org/

Publication series

NameIEEE International Geoscience and Remote Sensing Symposium (IGARSS)
PublisherIEEE
ISSN (Print)2153-6996
ISSN (Electronic)2153-7003

Conference

Conference38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Abbreviated title2018
CountrySpain
CityValencia
Period22/07/1827/07/18
Internet address

Keywords

  • Biophysical parameter retrieval
  • Feature selection
  • Hyperspectral images
  • Random forest

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