Detection of leaf structures in close-range hyperspectral images using morphological fusion

Gladys Villegas, Wenzhi Liao, Ronald Criollo, Wilfried Philips, Daniel Ochoa, Xin Huang, Jiayi Li, Jocelyn Chanussot

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods.
Original languageEnglish
Pages (from-to)325-332
Number of pages8
JournalGeo-spatial Information Science
Volume20
Issue number4
DOIs
Publication statusPublished - 29 Nov 2017

Keywords

  • hyperspectral images
  • fusion
  • morphology
  • leaf structures

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    Villegas, G., Liao, W., Criollo, R., Philips, W., Ochoa, D., Huang, X., Li, J., & Chanussot, J. (2017). Detection of leaf structures in close-range hyperspectral images using morphological fusion. Geo-spatial Information Science, 20(4), 325-332. https://doi.org/10.1080/10095020.2017.1399673