Hyperspectral data processing for repeat detection of small infestations of leafy spurge

Glenn, Nancy F.; Mundt, Jacob T.; Weber, Keith T.; Prather, Timothy S.; Lass, Lawrence W.; Pettingill, Jeffrey

doi:10.1016/j.rse.2005.01.003

Cited by 119 publications

(84 citation statements)

References 18 publications

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“…An accuracy target of 85% was recommended by [74], and an overall accuracy target of 85% with no individual class accuracy < 70% was suggested by [75]. Although there is no information related to the acceptable classification accuracies specified for remote detection of musk thistle, a threshold of the user's accuracy > 70% is required for mapping leafy spurge [51]. Based on these recommended accuracy guidelines, our classification of the April image did not meet the overall map accuracy target of 85%, but provided the recommended levels of the user's and producer's accuracies.…”

Section: Discussionmentioning

confidence: 99%

“…The literature indicates that there is a growing body of work on the hyperspectral mapping of non-native invasive species [50][51][52][53][54][55][56][57]. Most of the previous hyperspectral remote sensing studies dealing with mapping invasive plants have commonly utilized Airborne Visible Infrared Imaging Spectrometer, HyMap and Compact Airborne Spectrographic Imager images with spectral resolution ≥ 10 nm and spatial resolution ≥ 1.5 m. In addition to this, spectral angle mapper and mixture tuned matched filtering were commonly employed as the classification algorithms after dimension reduction or transformation of hyperspectral data, such as principal component analysis, minimum noise fraction or vegetation indices.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the non-native invasive species frequently distinguished and mapped in the USA include saltcedar (Tamarix spp.) [33,52,58,59], leafy spurge (Euphorbia esula L.) [51,56,57], spotted knapweed (Centaurea maculosa Sam.) [32,48,53] and yellow starthistle (Centaurea solstitialis) [50,54,55].…”

Section: Introductionmentioning

confidence: 99%

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Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Mırık¹,

Ansley²,

Steddom³

et al. 2013

Remote Sensing

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Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern offers several advantages, including repeatability, large area coverage, complete instead of sub-sampled assessments and greater cost-effectiveness over ground-based methods. It is critical for locating, early mapping and controlling small infestations before they reach economically prohibitive or ecologically significant levels over larger land areas. This study was designed to explore the ability of hyperspectral imagery for mapping infestation of musk thistle (Carduus nutans) on a native grassland during the preflowering stage in mid-April and during the peak flowering stage in mid-June using the support vector machine classifier and to assess and compare the resulting mapping accuracy for these two OPEN ACCESSRemote Sens. 2013, 5 613 distinctive phenological stages. Accuracy assessment revealed that the overall accuracies were 79% and 91% for the classified images at preflowering and peak flowering stages, respectively. These results indicate that repeated detection of the infestation extent, as well as infestation severity or intensity, of this noxious weed in a spatial and temporal context is possible using hyperspectral remote sensing imagery.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Mırık¹,

Ansley²,

Steddom³

et al. 2013

Remote Sensing

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“…Much research has been done on imagery collection techniques, rectification and georegistration of aerial imagery [3,4]. Rapid progress in data collection has been seen in recent years due to lower costs of data collection and improved technology [5].…”

Section: Introductionmentioning

confidence: 99%

Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

et al. 2016

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Abstract:The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

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“…MTMF estimates the similarity between spectra from given image pixels and the spectra of a target endmember (pure spectral signature from the focal species), estimating subpixel abundance of the target material or the likelihood for the target material is present in a given pixel [25]. Although some prior studies have used MTMF to detect invasive species [26][27][28][29], this technique has been poorly evaluated in ecosystems exhibiting complex canopy structure, such as tropical forests.…”

Section: Introductionmentioning

confidence: 99%

Determining Subcanopy Psidium cattleianum Invasion in Hawaiian Forests Using Imaging Spectroscopy

et al. 2016

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High-resolution airborne imaging spectroscopy represents a promising avenue for mapping the spread of invasive tree species through native forests, but for this technology to be useful to forest managers there are two main technical challenges that must be addressed: (1) mapping a single focal species amongst a diverse array of other tree species; and (2) detecting early outbreaks of invasive plant species that are often hidden beneath the forest canopy. To address these challenges, we investigated the performance of two single-class classification frameworks-Biased Support Vector Machine (BSVM) and Mixture Tuned Matched Filtering (MTMF)-to estimate the degree of Psidium cattleianum incidence over a range of forest vertical strata (relative canopy density). We demonstrate that both BSVM and MTMF have the ability to detect relative canopy density of a single focal plant species in a vertically stratified forest, but they differ in the degree of user input required. Our results suggest BSVM as a promising method to disentangle spectrally-mixed classifications, as this approach generates decision values from a similarity function (kernel), which optimizes complex comparisons between classes using a dynamic machine learning process.

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Hyperspectral data processing for repeat detection of small infestations of leafy spurge

Cited by 119 publications

References 18 publications

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

Determining Subcanopy Psidium cattleianum Invasion in Hawaiian Forests Using Imaging Spectroscopy

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