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

2 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.
LanguageEnglish
Pages325-332
Number of pages8
JournalGeo-spatial Information Science
Volume20
Issue number4
DOIs
Publication statusPublished - 29 Nov 2017

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leaves
fusion
multisensor fusion
infectious diseases
biology
quantitative analysis
remote sensing
manipulators
reflectance
pixel
pixels
environmental conditions
detection
plant biology
genetic effect
method
infection

Keywords

  • hyperspectral images
  • fusion
  • morphology
  • leaf structures

Cite this

Villegas, Gladys ; Liao, Wenzhi ; Criollo, Ronald ; Philips, Wilfried ; Ochoa, Daniel ; Huang, Xin ; Li, Jiayi ; Chanussot, Jocelyn. / Detection of leaf structures in close-range hyperspectral images using morphological fusion. In: Geo-spatial Information Science. 2017 ; Vol. 20, No. 4. pp. 325-332.
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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.",
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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, vol. 20, no. 4, pp. 325-332. https://doi.org/10.1080/10095020.2017.1399673

Detection of leaf structures in close-range hyperspectral images using morphological fusion. / Villegas, Gladys; Liao, Wenzhi; Criollo, Ronald; Philips, Wilfried; Ochoa, Daniel; Huang, Xin; Li, Jiayi; Chanussot, Jocelyn.

In: Geo-spatial Information Science, Vol. 20, No. 4, 29.11.2017, p. 325-332.

Research output: Contribution to journalArticle

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T1 - Detection of leaf structures in close-range hyperspectral images using morphological fusion

AU - Villegas, Gladys

AU - Liao, Wenzhi

AU - Criollo, Ronald

AU - Philips, Wilfried

AU - Ochoa, Daniel

AU - Huang, Xin

AU - Li, Jiayi

AU - Chanussot, Jocelyn

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AB - 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.

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KW - fusion

KW - morphology

KW - leaf structures

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