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Binary partition tree as a hyperspectral segmentation tool for tropical rainforests

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Individual tree crown delineation in tropical forests is of great interest for ecological applications. In this paper we propose a method for hyperspectral image segmentation based on binary tree partitioning. The initial partition is obtained from a watershed transformation in order to make the method computationally more efficient. Then we use a non-parametric region model based on histograms to characterize the regions and the diffusion distance to define the region merging order. The pruning strategy is based on the discontinuity of size increment observed when iteratively merging the regions. The segmentation quality is assessed visually and appears to perform well on most cases, but tree delineation could be improved by including structural information derived from LiDAR data.

Original languageEnglish (US)
Title of host publicationIGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6368-6371
Number of pages4
ISBN (Electronic)9781467311595
DOIs
StatePublished - 2012
Externally publishedYes
Event32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Germany
Duration: Jul 22 2012 → Jul 27 2012

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
Country/TerritoryGermany
CityMunich
Period7/22/12 → 7/27/12

Keywords

  • Binary partition tree
  • Carnegie airborne observatory
  • hyperspectral imagery
  • segmentation
  • tree crown delineation
  • tropical forest

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences

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