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Journal of Applied Remote Sensing • new

Identifying relevant hyperspectral bands using Boruta: a temporal analysis of water hyacinth biocontrol
Author(s): Na’eem Hoosen Agjee; Riyad Ismail; Onisimo Mutanga
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Paper Abstract

Water hyacinth plants (<italic<Eichhornia crassipes</italic<) are threatening freshwater ecosystems throughout Africa. The <italic<Neochetina</italic< spp. weevils are seen as an effective solution that can combat the proliferation of the invasive alien plant. We aimed to determine if multitemporal hyperspectral data could be utilized to detect the efficacy of the biocontrol agent. The random forest (RF) algorithm was used to classify variable infestation levels for 6 weeks using: (1) all the hyperspectral bands, (2) bands selected by the recursive feature elimination (RFE) algorithm, and (3) bands selected by the Boruta algorithm. Results showed that the RF model using all the bands successfully produced low-classification errors (12.50% to 32.29%) for all 6 weeks. However, the RF model using Boruta selected bands produced lower classification errors (8.33% to 15.62%) than the RF model using all the bands or bands selected by the RFE algorithm (11.25% to 21.25%) for all 6 weeks, highlighting the utility of Boruta as an all relevant band selection algorithm. All relevant bands selected by Boruta included: 352, 754, 770, 771, 775, 781, 782, 783, 786, and 789 nm. It was concluded that RF coupled with Boruta band-selection algorithm can be utilized to undertake multitemporal monitoring of variable infestation levels on water hyacinth plants.

Paper Details

Date Published: 1 June 2016
PDF: 16 pages
J. Appl. Remote Sens. 10(4) 042002 doi: 10.1117/1.JRS.10.042002
Published in: Journal of Applied Remote Sensing Volume 10, Issue 4
Show Author Affiliations
Na’eem Hoosen Agjee, Univ. of KwaZulu-Natal (South Africa)
Riyad Ismail, Univ. of KwaZulu-Natal (South Africa)
Onisimo Mutanga, Univ. of KwaZulu-Natal (South Africa)

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