Hyperspectral Image Processing Methods

Yoon, Seung-Chul; Park, Bosoon

doi:10.1007/978-1-4939-2836-1_4

Cited by 14 publications

(10 citation statements)

References 60 publications

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“…Black references were gathered by capturing and averaging 10 hyperspectral frames with the camera shutter closed and white references were taken from a white lambertian reference panel included in each image. Intensity values for each pixel at each wavelength were converted to reflectance values by subtracting the dark reference and dividing the result by the difference between the white and dark references (Yoon & Park, 2015). The resulting reflectance values were then normalized by dividing each spectrum by its L_2 norm, or the square root of the sum of the squares of that signature, following the equation:

x_{normalnorm} = \frac{x}{\sum_{i = 1}^{n} {((), {x_{i}}^{2})}^{1 / 2}}

where x is the full vector of reflectance data in image, i is the response band, n is the total number of measured wavelengths, and x i is the full vector of reflectance data in image for the response band i .…”

Section: Methodsmentioning

confidence: 99%

Utilizing spatial variability from hyperspectral imaging to assess variation in maize seedlings

Tirado

Dennis

Springer

2021

The Plant Phenome Journal

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There is significant enthusiasm about the potential for hyperspectral imaging to document variation among plant species, genotypes, or growing conditions. However, in many cases the application of hyperspectral imaging is performed in highly controlled situations that focus on a flat portion of a leaf or side-views of plants that would be difficult to obtain in field settings. We were interested in assessing the potential for applying hyperspectral imaging from a top-down view to document variation in genotypes and abiotic stresses for maize (Zea mays L.) seedlings grown in controlled environments. A top-down image of a maize seedling includes a view into the funnel-like whorl at the center of the plant with several leaves radiating outward. There is substantial variability in the reflectance profile of different portions of this plant. To deal with the variability in reflectance profiles that arises from this morphology we implemented a method that divides the longest leaf into 10 segments of equal length from the center to the leaf tip. We show that there is large variability in the hyperspectral profiles across leaf segments, which are masked when performing whole-plant averages as tend to be done when analyzing hyperspectral data. We found that using these segments provides improved ability to discriminate different genotypes (B73, Mo17, Ki11, MS71, PH207) and abiotic stress conditions (heat, cold, or salinity stress) for maize seedlings. This provides an approach that can be implemented to help classify genotype and environmental variation for maize seedlings from a top-down view such as that which would be collected in field settings. 1 INTRODUCTION Abiotic stresses cause major yield declines across many crops and can limit production by up to 70% (Boyer, 1982; Majeed & Muhammad, 2019). Advances in molecular tools have greatly facilitated breeders in efficiently identifying and Abbreviations: DAS, days after sowing; PCA, principal component analysis; RGB, red-green-blue; SVM, support vector machine; ZT, Zeitgeber Time. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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x_{normalnorm} = \frac{x}{\sum_{i = 1}^{n} {((), {x_{i}}^{2})}^{1 / 2}}

Section: Methodsmentioning

confidence: 99%

Utilizing spatial variability from hyperspectral imaging to assess variation in maize seedlings

Tirado

Dennis

Springer

2021

The Plant Phenome Journal

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“…Subsequently, the data can be classified to identify the pixels/spectra useful for the analysis. Regression techniques can also be applied, to estimate a reference parameters in particular, in recent years techniques of chemometric and multivariate analysis have been applied to hyperspectral images (Yoon and Park, 2015).…”

Section: Hyperspectral Image Processingmentioning

confidence: 99%

In-field hyperspectral imaging: An overview on the ground-based applications in agriculture

Benelli

Cevoli

Fabbri

2020

J Agricult Engineer

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The measurement of vegetation indexes that characterise the plants growth, assessing the fruit ripeness or detecting the presence of defects and diseases, is a key factor to gain high quality of fruit or vegetables. Such evaluation can be carried out using both destructive and non destructive techniques. Among non-destructive techniques, hyperspectral imaging (HSI), combining image analysis and visible/near-infrared spectroscopy, looks particularly useful. Many studies have been published concerning the use of hyperspectral cameras in the agronomic and food field, especially in controlled laboratory conditions. Conversely, few studies described the application of HSI technology directly in field, especially involving ground-based systems. Results suggest that this technique could be particularly useful, even if the role of environmental variables has to be considered (e.g., intensity and incidence of solar radiation, wind or the soil in the background). In this paper, recent in-field HSI applications based on ground systems are reviewed.

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“…The SNV does not require a class reference spectrum as the MSC does, because SNV subtracts the sample mean, and divides by the standard deviation for each sample. 17 Tungsten halogen light normalised spectra show slight rep effects; however, after applying SNV, more sample homogeneity from different experiments is observed, and reducing the rep effect on the spectra (see Supplementary Figure S2). This step can be done when more than one species of bacteria are present in an image, without blurring the proceeding cluster boundaries.…”

Section: Data Preprocessingmentioning

confidence: 99%

Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image

Eady

Park

2018

J. Spectral Imaging

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Salmonella is a leading cause of foodborne illness. Traditional detection methods require lengthy incubation periods or expensive reagent kits. Hyperspectral microscope images (HMIs) have been previously investigated as a method for early and rapid detection of bacteria by using a spectral signature that is unique to the organism. Previous HMI use with bacteria has consisted of supervised classification with hypercubes collected for single culture images isolated from highly selective growth media. In order to move forward with HMI as a detection tool in the food industry, unsupervised classification of bacteria cells in mixed culture HMIs was investigated. Four foodborne bacteria cultures, S. Typhimurium (ST) E. coli (Ec), S. aureus (Sa) and L. innocua (Li) were combined in seven different culture combinations with HMIs collected between 450 nm and 800 nm. A k-means divisive cluster analysis (CA) was implemented and mixed culture image sets were found to contain between two and four clusters. CA cluster accuracy was obtained by assigning a dummy variable of the proposed CA classification, then carrying out a discriminant analysis. From the mixed culture HMIs, 700 bacteria cells were classified and accuracies were between 91.92% and 100%, with six of the seven HMI sets resulting in > 97% accuracies. A distance measure between clusters was applied to identify unknown clusters based on single culture reference samples of the four bacteria used. Results showed that the CA has potential for unsupervised classification of bacteria cells, but the distance metric was not an adequate method for identifying the unknown cluster based on reference spectra, potentially due to the collinearity amongst bacteria spectra.

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Hyperspectral Image Processing Methods

Cited by 14 publications

References 60 publications

Utilizing spatial variability from hyperspectral imaging to assess variation in maize seedlings

Utilizing spatial variability from hyperspectral imaging to assess variation in maize seedlings

In-field hyperspectral imaging: An overview on the ground-based applications in agriculture

Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image

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