Detecting and Identifying Spectral Anomalies Using Wavelet Processing

Stork, Chris L.; Veltkamp, David J.; Kowalski, Bruce R.

doi:10.1366/0003702981942681

Cited by 13 publications

(9 citation statements)

References 13 publications

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“…Besides these main streams, other applications such as recognition of signal discontinuities [36] or contrast enhancement of edges in image processing [37] have been reported.…”

Section: Applications In Analytical Chemistrymentioning

confidence: 99%

Wavelet transform applications in analytical chemistry

Ehrentreich

2001

Anal Bioanal Chem

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The wavelet transform has been established with the Fourier transform as a data-processing method in analytical chemistry. The main fields of application in analytical chemistry are related to denoising, compression, variable reduction, and signal suppression. Analytical applications were selected showing prospects and limitations of the wavelet transform. An important aspect consists in showing the advantage of wavelet transform over Fourier transform with respect to dual localization of a signal in both the original and the transformed domain enabling principal new application fields in comparison with Fourier transform.

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“…Besides these main streams, other applications such as recognition of signal discontinuities [36] or contrast enhancement of edges in image processing [37] have been reported.…”

Section: Applications In Analytical Chemistrymentioning

confidence: 99%

Wavelet transform applications in analytical chemistry

Ehrentreich

2001

Anal Bioanal Chem

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“…Kosanovich and Piovoso (1997), Luo, Misra, and Himmelblau (1999) and Shao, Jia, Martin and Morris (1999) have also had success with multiscale PCA using wavelets. Stork, Veltkamp, and Kowalski (1998) applied wavelet transforms to NIR spectra in order to detect spectral features characterised by wavelength and scale. Wavelet functions are localised, however, so the wavelet transform is not invariant to time delays.…”

Section: Introductionmentioning

confidence: 99%

Spectral principal component analysis of dynamic process data

Thornhill

Shah

Huang

et al. 2002

Control Engineering Practice

130

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This article describes principal component analysis (PCA) of the power spectra of data from chemical processes. Spectral PCA can be applied to the measurements from a whole unit or plant because spectra are invariant to the phase lags caused by time delays and process dynamics. The same comment applies to PCA using autocovariance functions, which was also studied. Two case studies are presented. One was derived from simulation of a pulp process. The second was from a refinery involving 37 tags. In both cases, PCA clusters were observed which were characterised by distinct spectral features. Spectral PCA was compared with PCA using autocovariance functions. The performance was similar, and both offered an improvement over PCA using the time domain signals even when time shifting was used to align the phases. r

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“…At coarse resolutions, the details correspond to the larger structures. The spectral response is viewed at increasingly finer detail and provides a better representation of the transformed signal (Stork et al 1998). Nine features were extracted from each reflectance measurement.…”

Section: Methodsmentioning

confidence: 99%

Effect of purple (Cyperus rotundus) and yellow nutsedge (C. esculentus) on growth and reflectance characteristics of cotton and soybean

Leon

Shaw²,

Bruce

et al. 2003

Weed Science

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Because of interest in monitoring crop response to weed interference, greenhouse experiments were conducted to evaluate interference of purple and yellow nutsedge on the growth, development, and spectral response of cotton and soybean. Cotton fresh weight was reduced 9 to 42% compared with the control when grown with yellow and purple nutsedge. Fresh weight of soybean was reduced 27 to 60% when it emerged simultaneously with yellow nutsedge and 45 to 63% when it emerged 7 d after yellow nutsedge. Soybean fresh weight was reduced 30 to 35% when it emerged simultaneously with purple nutsedge and 44 to 72% when it emerged 7 d after purple nutsedge. Reflectance data were analyzed using wavelet transformation techniques with the HAAR mother wavelet. Nine extracted features from the cotton and soybean leaf reflectance measurements were used to classify single-leaf cotton and soybean reflectance measurements to predict whether cotton or soybean was growing in the presence or absence of purple and yellow nutsedge. After training the system, the ability to separate leaf reflectance measurements of crops growing weed free from those growing in the presence of purple and yellow nutsedge was tested using cross-validation with the nearest mean classifier. Cross-validation accuracy results for cotton were 62 to 70%. Cross-validation accuracy for soybean and yellow nutsedge was similar, regardless of emergence, and ranged from 60 to 71%. Features extracted from the soybean reflectance measurements were not as effective in classifying soybean leaf reflectance measurements based on the presence or absence of purple nutsedge. A decrease in accuracy was observed for both simultaneous and delayed soybean emergence in purple nutsedge fresh weight categories from more than 2,560 g to more than 3,420 g. Overall, the system correctly classified soybean emerging simultaneously with purple nutsedge 58 to 74% and soybean emerging 7 d after purple nutsedge 53 to 67%. These results indicate the potential of differentiating crops under stress using spectral reflectance, although refinements to the system must be made before it is field ready.

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Detecting and Identifying Spectral Anomalies Using Wavelet Processing

Cited by 13 publications

References 13 publications

Wavelet transform applications in analytical chemistry

Wavelet transform applications in analytical chemistry

Spectral principal component analysis of dynamic process data

Effect of purple (Cyperus rotundus) and yellow nutsedge (C. esculentus) on growth and reflectance characteristics of cotton and soybean

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