Detection of leaf structures in close-range hyperspectral images using morphological fusion. Issue 4 (2nd October 2017)

• Record Type: Journal Article
• Authors: Villegas, Gladys; Liao, Wenzhi; Criollo, Ronald; Philips, Wilfried; Ochoa, Daniel
• Is Part Of: Geo-spatial information science. Volume 20:Issue 4(2017)
• Page Start: 325
• Page End: 332
• Publication Date: 2017-10-02
• Subjects: Hyperspectral -- fusion -- morphology -- plant biology; Geographic information systems -- Periodicals; Cartography -- Data processing -- Periodicals; Surveying -- Data processing -- Periodicals; Remote sensing -- Periodicals; 526.0285
• DOI: 10.1080/10095020.2017.1399673
• Languages: English
• ISSNs: 1009-5020

Abstract

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.