Banana disease detection by fusion of close range hyperspectral image and high-resolution RGB image

Wenzhi Liao, Daniel Ochoa, Yongqiang Zhao, Gladys Maria Villegas Rugel, Wilfried Philips

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

11 Scopus citations

Abstract

Early detection of banana disease can limit the spread of disease, as well as reduce the treatment costs. Current methods focus on either manually interpretation or calculation of spectral indices (e.g., the normalized difference vegetation index). In this paper, we exploit the fusion of close range hyperspectral (HS) image and high-resolution (HR) visible RGB image for potential disease detection in banana leaves. Our approach applies the joint bilateral filter to transfer the textural structures of HR RGB image to low-resolution HS image and obtain an enhanced HS image. Initial experimental results on Musa acuminata (banana) leaf images demonstrate the efficiency of our fusion approach, with significant improvements over either single data source or some conventional methods.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1744-1747
Number of pages4
ISBN (Electronic)9781538671504
DOIs
StatePublished - 31 Oct 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2018-July

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/TerritorySpain
CityValencia
Period22/07/1827/07/18

Keywords

  • Banana diseases
  • Close range hyperspectral image
  • Data fusion
  • Morphology

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