Mapping Bugweed (Solanum mauritianum) Infestations in Pinus patula Plantations Using Hyperspectral Imagery and Support Vector Machines

Atkinson, Jonathan Tom; Ismail, Riyad; Robertson, Mark P.

doi:10.1109/jstars.2013.2257988

Cited by 47 publications

(25 citation statements)

References 65 publications

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“…As hyperspectral data constitute a source of ongoing information about spectral reflection, they provide a lot of information about the biophysical and chemical characteristics of the analyzed vegetation [13][14][15]. Either hyperspectral satellite data (e.g., Hyperion [16] and CHRIS [15,17]) or aerial data (e.g., APEX [18] and AISA [19,20]) are used, depending on the size of the research area and the canopy characteristics of the identified vegetation. Airborne data are more useful for the detection of small, less compact patches of plant species because of their high spatial resolution [16].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

2020

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Invasive and expansive plant species are considered a threat to natural biodiversity because of their high adaptability and low habitat requirements. Species investigated in this research, including Solidago spp., Calamagrostis epigejos, and Rubus spp., are successfully displacing native vegetation and claiming new areas, which in turn severely decreases natural ecosystem richness, as they rapidly encroach on protected areas (e.g., Natura 2000 habitats). Because of the damage caused, the European Union (EU) has committed all its member countries to monitor biodiversity. In this paper we compared two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to identify Solidago spp., Calamagrostis epigejos, and Rubus spp. on HySpex hyperspectral aerial images. SVM and RF are reliable and well-known classifiers that achieve satisfactory results in the literature. Data sets containing 30, 50, 100, 200, and 300 pixels per class in the training data set were used to train SVM and RF classifiers. The classifications were performed on 430-spectral bands and on the most informative 30 bands extracted using the Minimum Noise Fraction (MNF) transformation. As a result, maps of the spatial distribution of analyzed species were achieved; high accuracies were observed for all data sets and classifiers (an average F1 score above 0.78). The highest accuracies were obtained using 30 MNF bands and 300 sample pixels per class in the training data set (average F1 score > 0.9). Lower training data set sample sizes resulted in decreased average F1 scores, up to 13 percentage points in the case of 30-pixel samples per class.

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Section: Introductionmentioning

confidence: 99%

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

2020

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“…Atkinson et al [16] determined the utility of SVMs for detecting and mapping the presence of bug weed such as Solanum mauritianum within the Pinus patula plantations. Higher classification accuracy was achieved and the optimal subset of wavebands for the detection of bug weed was identified using SVM with recursive feature elimination approach.…”

Section: Related Workmentioning

confidence: 99%

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

Prema¹,

Murugan²

2016

ELCVIA

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Weed management is the most significant process in the agricultural applications to improve the crop productivity rate and reduce the herbicide application cost. Existing weed detection techniques does not yield better performance due to the complex background and illumination variation. Hence, there arises a need for the development of effective weed identification technique. To overcome this drawback, this paper proposes a novel Angular Texture Pattern (ATP) Extraction Method for crop and weed discrimination using curvelet transformation. In our proposed work, Adaptive Median Filter (AMF) is used for filtering the impulse noise from the image. Plant image identification is performed using green pixel extraction and K-means clustering. Wrapping based Curvelet transform is applied to the plant image. Feature extraction is performed to extract the angular texture pattern of the plant image. Particle Swarm Optimization (PSO)-based Differential Evolution Feature Selection (DEFS) approach is applied to select the optimal features. Then, the selected features are learned and passed through an RVM-based classifier to find out the weed. Edge detection and contouring is performed to identify the weed in the plant image. The fuzzy rule-based approach is applied to detect the low, medium and high levels of the weed patchiness. From the experimental results, it is clearly observed that the accuracy of the proposed approach is higher than the existing Support Vector Machine (SVM) based approaches. The proposed approach achieves higher accuracy, sensitivity, specificity, Hausdorff distance, Jaccard distance and Dice distance.

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“…More recent results by Eddy et al, (2014) showed the potential of hyperspectral imagery in classifying weeds with accuracies ranging from 88% to 94% using a neural network classification algorithm. Atkinson et al, (2014) showed classification accuracies of 93% using AISA Eagle hyperspectral data and support vector machines to map bugweed in forestry plantations in South Africa. also assessed the utility of AISA Eagle hyperspectral data to discriminate bugweed from a variety of forest species in South Africa.…”

Section: Introductionmentioning

confidence: 99%

Assessing the utility of the spot 6 sensor in detecting and mapping <i>Lantana camara</i> for a community clearing project in KwaZulu-Natal, South Africa

Oumar¹

2016

SA J of Geomatics

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Mapping Bugweed (Solanum mauritianum) Infestations in Pinus patula Plantations Using Hyperspectral Imagery and Support Vector Machines

Cited by 47 publications

References 65 publications

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

Assessing the utility of the spot 6 sensor in detecting and mapping <i>Lantana camara</i> for a community clearing project in KwaZulu-Natal, South Africa

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