Standardization of near-infrared spectra in the wavelet domain

Walczak, Beata; Bouveresse, E.; Massart, D.L.

doi:10.1016/s0169-7439(96)00075-5

Cited by 99 publications

(52 citation statements)

References 22 publications

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“…The wavelet transform comprises a processing tool that decomposes the original signal (i.e., image) in time and frequency domains, thus representing the image in multi-scales [24][25][26]. Once the wavelet components are computed, the noise is concentrated at the lowest scales while the continuum remains at the highest scales.…”

Section: Waveletsmentioning

confidence: 99%

Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data

Scafutto

Filho

2018

Remote Sensing

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Methane (CH 4 ) display spectral features in several regions of the infrared range (0.75-14 µm), which can be used for the remote mapping of emission sources through the detection of CH 4 plumes from natural seeps and leaks. Applications of hyperspectral remote sensing techniques for the detection of CH 4 in the near and shortwave infrared (NIR-SWIR: 0.75-3 µm) and longwave infrared (LWIR: 7-14 µm) have been demonstrated in the literature with multiple sensors and scenarios. However, the acquisition and processing of hyperspectral data in the midwave infrared (MWIR: 3-5 µm) for this application is rather scarce. Here, a controlled field experiment was used to evaluate the potential for CH 4 plume detection in the MWIR based on hyperspectral data acquired with the SEBASS airborne sensor. For comparison purposes, LWIR data were also acquired simultaneously with the same instrument. The experiment included surface and undersurface emission sources (ground stations), with flow rates ranging between 0.6-40 m 3 /h. The data collected in both ranges were sequentially processed using the same methodology. The CH 4 plume was detected, variably, in both datasets. The gas plume was detected in all LWIR images acquired over nine gas leakage stations. In the MWIR range, the plume was detected in only four stations, wherein 18 m 3 /h was the lowest flux sensed. We demonstrate that the interference of target reflectance, the low contrast between plume and background and a low signal of the CH 4 feature in the MWIR at ambient conditions possibly explain the inferior results observed for this range when compared to LWIR. Furthermore, we show that the acquisition time and weather conditions, including specific limits of temperature, humidity, and wind speed, proved critical for plume detection using daytime MWIR hyperspectral data.

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Section: Waveletsmentioning

confidence: 99%

Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data

Scafutto

Filho

2018

Remote Sensing

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“…To understand our approach to standardization, it is necessary to briefly review the standardization method reported by Walczak et al [26], the first application of a wavelet transform in calibration transfer. The main idea is illustrated in Figure 1, including the following three steps.…”

Section: Standardization In the Wavelet Domain (Swd)mentioning

confidence: 99%

“…SWD will take good advantage of the local character from the wavelet transform. Therefore the choice of the lowest resolution level, j = 1, seems to be the proper one [26]. Here the original spectra can be divided into approximation and detail coefficients in the wavelet domain, one including mostly low-frequency component information and the other including mostly high-frequency noisy information.…”

Section: Standardization In the Wavelet Domain (Swd)mentioning

confidence: 99%

“…Another solution to the problem is to standardize spectra using the wavelet transform (WT), which has both a local character (in both the frequency and time domains) and a noise-filtering effect. A standardization method called standardization in the wavelet domain (SWD) was proposed by Walczak et al [26]. In this method, however, full robustness to spectral noise is still not achieved and the other benefits obtained in conventional PDS transfer are partially lost because of the use of MLR instead of PDS for standardization in the wavelet domain.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet hybrid direct standardization of near‐infrared multivariate calibrations

Tan

Brown

2001

Journal of Chemometrics

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SUMMARYA novel standardization method called wavelet hybrid direct standardization (WHDS) is proposed. It is based on a new wavelet reconstruction algorithm in which approximation and detail spectra are reconstructed separately. Piecewise direct standardization (PDS) and direct standardization (DS) are used to correct the differences of original spectra by transforming the reconstructed approximation and detail spectra respectively. The proposed method is applied to near-infrared data and its performance is compared with current methods. Result shows that, using the wavelet multi-resolution technique and combining PDS and DS, the WHDS algorithm allows a more robust and reliable means for standardization when transfer standards are available.

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“…14 Among them, one noteworthy study is wavelet transform-based standardization (WTS). In this method, NIR spectral data on a wavelength basis are transformed into a wavelet basis because a wavelet transform offers advantages in analyzing local spectral differences.…”

Section: Introductionmentioning

confidence: 99%

Calibration Transfer between Two Near-Infrared Spectrometers Based on a Wavelet Packet Transform

Tan

2007

ANAL. SCI.

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A standardization algorithm, called WPTS, which is based on a wavelet packet transform (WPT) and entropy criteria, is proposed to transfer spectra between two near-infrared spectrometers. First, the spectra of the standardization samples measured on both spectrometers are collected and the difference mean spectrum is calculated; then, a wavelet packet transform is performed and a best WPT-tree is generated, of which a node is selected to establish a transfer matrix. Once the transfer matrix has been built, spectra measured on one spectrometer can be successfully transferred to another spectrometer as if they have been measured directly on the latter. By comparison, the proposed WPTS can reach a transfer performance comparable to WTS1, which is the best existing method, and better than WTS2 and classical PDS by a simpler technique of variable selection and lower computational cost of calibration transfer.

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Standardization of near-infrared spectra in the wavelet domain

Cited by 99 publications

References 22 publications

Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data

Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data

Wavelet hybrid direct standardization of near‐infrared multivariate calibrations

Calibration Transfer between Two Near-Infrared Spectrometers Based on a Wavelet Packet Transform

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