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Discrete wavelets transform for improving greenness image segmentation in agricultural images
University of Complutense Madrid, Spain.
University of Complutense Madrid, Spain.
University of Complutense Madrid, Spain.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7305-956X
2015 (English)In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 118, 396-407 p.Article in journal (Refereed) Published
Abstract [en]

We propose a segmentation strategy for agricultural images in order to successfully distinguish between both soil and green parts, the last ones including weeds and crop plants, based on discrete wavelets transform. Vegetation indices have been commonly used for greenness image segmentation, but improvements are still possible. In agricultural images weeds and crops plants display high spatial variability with irregular and random distributions. Textures descriptors have the ability to capture this information, which conveniently combined with vegetation indices improve the greenness segmentation results. The proposed approach consists of the following steps: (a) greenness extraction based on vegetation indices; (b) application of the wavelets transform to the resulting image, allowing the extraction of spatial structures in three bands (horizontal, vertical and diagonal) containing detailed information; (c) use of texture descriptors to capture the spatial variability in the three bands; (d) combination of greenness and texture information, in the approximation coefficients of the wavelets transform, for enhancing plants (weeds and crops) identification; and (e) application of an image thresholding method for final image identification. The wavelets transform allows both capture of spatial texture and its fusion with the greenness information, making the main contribution of this paper. This approach is especially useful when the quality of imaging greenness is low. It has been favorably compared against existing strategies, obtaining better results, quantified by 4,5%. (C) 2015 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD , 2015. Vol. 118, 396-407 p.
Keyword [en]
Textures; Image segmentation; Wavelets; Agricultural images; Thresholding; Binarization
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-123158DOI: 10.1016/j.compag.2015.09.011ISI: 000364603500041OAI: oai:DiVA.org:liu-123158DiVA: diva2:877394
Note

Funding Agencies|European Union [245986]; Ministerio de Educacion y Ciencia of Spain within the Plan Nacional de I+D+i [AGL2014-52465-C4-3-R]

Available from: 2015-12-07 Created: 2015-12-04 Last updated: 2016-08-31

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Zitinski Elias, Paula
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Media and Information TechnologyFaculty of Science & Engineering
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