Identifying optimal waveband positions for discriminating Parthenium hysterophorus using hyperspectral data

Ullah, Saleem; Shakir, Muhammad; Iqbal, Muhammad Shahid; Iqbal, Arshad; Ali, Muhammad Zeeshan; Shafique, Muhammad; Rehman, Abdul; Godwin, Julien

doi:10.1016/j.ecoinf.2021.101362

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…Dimension reduction is a critical consideration for the improvement of classification performance on hyperspectral data (Nidamanuri, 2020;Ullah et al, 2021). Various studies have indicated that no single technique universally proven to be superior for optimal feature selection, and inconsistent results have usually been obtained using different techniques (Taylor et al, 2012;Fernandes et al, 2013).…”

Section: Impact Of Dimension Reduction On Spectral Discrimination Of ...mentioning

confidence: 99%

“…However, the accurate detection of herbaceous plants remains difficult owing to the similarity of spectra and texture among species (Schmidt and Skidmore, 2003;Jana et al, 2017). Hyperspectral instruments can significantly improve the discrimination by capitalizing on the differences in vegetation spectra associated with biophysical or biochemical characteristics (Subhashni et al, 2012;Ullah et al, 2021). Most recently, hyperspectral imaging by unmanned aerial vehicles (UAV) has shown great potential for characterizing spatial and temporal dynamics of vegetation, and is gaining traction in studies of mapping weed species such as Mikania micrantha Kunth (Huang et al, 2021), Phragmites australis and other wetland plant species (Du et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Discriminating Stellera chamaejasme in alpine grasslands using UAV hyperspectral imagery and multivariate analysis

Wang,

Liu,

et al. 2023

Front. Ecol. Evol.

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Stellera chamaejasme is highly invasive and causes a significant threat to alpine grassland on the Qinghai-Tibet Plateau. It is important to determine its distribution pattern in order to stem the rapid invasion of this toxic weed. In the present study, strategies for mapping S. chamaejasme based on UAV Resonon hyperspectral imaging were assessed in combination with dimension reduction, clustering and ordination analysis, and spectral separability measurement. Field spectrometry analysis showed that the hierarchical procedure of Kruskal-Wallis test and Dunn’s post hoc test, CART and JM distance can efficiently select the minimum optimal wavelengths for S. chamaejasme discrimination and considerably reduce the dimensions of hyperspectral imagery. DCA and RDA ordination revealed that S. chamaejasme identification at the species level is difficult even using hyperspectral feature bands. The reaggregating of TWINSPAN ecological communities based on the criterion of JM distance > 1.9 can result in spectrally delineated S. chamaejasme communities and the co-existing species community. Applying three algorithms of MLC, RF, and SVM, the performance of S. chamaejasme classification based on JM-TWINSPAN schemes was significantly improved compared to that based on TWINSPAN schemes. Of these, RF and JM-TWINSPAN achieved the best classification result (OA = 91.00%, Kappa = 0.83, AD = 14.0%, and QD = 1.2%). The result indicates that the promising accuracy can be achieved in accurate mapping of S. chamaejasme by a multivariate approach, which combines ecological analysis and separability measurement with image classification.

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Section: Impact Of Dimension Reduction On Spectral Discrimination Of ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discriminating Stellera chamaejasme in alpine grasslands using UAV hyperspectral imagery and multivariate analysis

Wang,

Liu,

et al. 2023

Front. Ecol. Evol.

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Species classification from hyperspectral leaf information using machine learning approaches

Song

Wang

2023

Ecological Informatics

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Detection of Parthenium Weed (Parthenium hysterophorus L.) and Its Growth Stages Using Artificial Intelligence

Costello

Osunkoya²,

Sandino

et al. 2022

Agriculture

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Parthenium weed (Parthenium hysterophorus L. (Asteraceae)), native to the Americas, is in the top 100 most invasive plant species in the world. In Australia, it is an annual weed (herb/shrub) of national significance, especially in the state of Queensland where it has infested both agricultural and conservation lands, including riparian corridors. Effective control strategies for this weed (pasture management, biological control, and herbicide usage) require populations to be detected and mapped. However, the mapping is made difficult due to varying nature of the infested landscapes (e.g., uneven terrain). This paper proposes a novel method to detect and map parthenium populations in simulated pastoral environments using Red-Green-Blue (RGB) and/or hyperspectral imagery aided by artificial intelligence. Two datasets were collected in a control environment using a series of parthenium and naturally co-occurring, non-parthenium (monocot) plants. RGB images were processed with a YOLOv4 Convolutional Neural Network (CNN) implementation, achieving an overall accuracy of 95% for detection, and 86% for classification of flowering and non-flowering stages of the weed. An XGBoost classifier was used for the pixel classification of the hyperspectral dataset—achieving a classification accuracy of 99% for each parthenium weed growth stage class; all materials received a discernible colour mask. When parthenium and non-parthenium plants were artificially combined in various permutations, the pixel classification accuracy was 99% for each parthenium and non-parthenium class, again with all materials receiving an accurate and discernible colour mask. Performance metrics indicate that our proposed processing pipeline can be used in the preliminary design of parthenium weed detection strategies, and can be extended for automated processing of collected RGB and hyperspectral airborne unmanned aerial vehicle (UAV) data. The findings also demonstrate the potential for images collected in a controlled, glasshouse environment to be used in the preliminary design of invasive weed detection strategies in the field.

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Identifying optimal waveband positions for discriminating Parthenium hysterophorus using hyperspectral data

Cited by 4 publications

References 39 publications

Discriminating Stellera chamaejasme in alpine grasslands using UAV hyperspectral imagery and multivariate analysis

Discriminating Stellera chamaejasme in alpine grasslands using UAV hyperspectral imagery and multivariate analysis

Species classification from hyperspectral leaf information using machine learning approaches

Detection of Parthenium Weed (Parthenium hysterophorus L.) and Its Growth Stages Using Artificial Intelligence

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