Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection

Yang, Guofeng; Dai, Junhu; Liu, Xiangjun; Meng, Chen; Wu, Xiaolong

doi:10.1039/c9ay02052g

Cited by 24 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wavelet analysis has good localization properties in both the time domain and frequency domain. Wavelet analysis highlights some features of the image through a "time-frequency" window that changes with frequency and a local "focusing" analysis of time (space) frequency, which can decompose a signal into an independent part of the signal to space and time without losing the information contained in the original signal [53]. The basic formula of the wavelet transform is described as follows:…”

Section: Wavelet Transformmentioning

confidence: 99%

Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up Areas

Zhou

Zhang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

Urban built-up areas are not only the embodiment of urban expansion but also the main space carrier of urban activities. Accurate extraction of urban built-up areas is of great practical significance for measuring the urbanization process and judging the urban environment. It is difficult to identify urban built-up areas objectively and accurately with single data. Therefore, to evaluate urban built-up areas more accurately, this study uses the new method of fusing wavelet transforms and images on the basis of utilization of the POI data of March 2019 and the Luojia1-A data from October 2018 to March 2019. to identify urban built-up areas. The identified urban built-up areas are mainly concentrated in the areas with higher urbanization level and night light value, such as the northeast of Dianchi Lake and the eastern bank around the Dianchi Lake. It is shown in the accuracy verification result that the classification accuracy identified by night-light data of urban build-up area accounts for 84.00% of the total area with the F1 score 0.5487 and the Classification accuracy identified by the fusion of night-light data and POI data of urban build-up area accounts for 96.27% of the total area with the F1 score 0.8343. It is indicated that the built-up areas identified after image fusion are significantly improved with more realistic extraction results. In addition, point of interest (POI) data can better account for the deficiency in nighttime light (NTL) data extraction of urban built-up areas in the urban spatial structure, making the extraction results more objective and accurate. The method proposed in this study can extract urban built-up areas more conveniently and accurately, which is of great practical significance for urbanization monitoring and sustainable urban planning and construction.

show abstract

Section: Wavelet Transformmentioning

confidence: 99%

Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up Areas

Zhou

Zhang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…[ 5 ] Wavelet transform can be also used for baseline discrimination. [ 7,8 ] The baseline fitting algorithms based on wavelets are rather complex, and their efficiency remains questionable.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric least‐squares baseline algorithm with peak screening for automatic processing of the Raman spectra

Korepanov

2020

J Raman Spectroscopy

View full text Add to dashboard Cite

The signal in Raman spectroscopy has three components: vibrational bands, noise, and luminescence. Although the noise can be easily separated from the Raman bands either by a variety of algorithms or by adjusting the measurement conditions, to distinguish between the luminescence and the Raman peaks is a challenging task. Even though a number of algorithms have been proposed for this purpose, most of them show poor performance when applied to broad overlapping Raman bands with extended tails. A practical example of such a case can be found in many carbon-based nanoscale materials with some degree of disorder, where the two close-lying D and G bands dominate the spectrum. Conventional asymmetric least-squares algorithms generate a baseline with penalizing the second derivative of the baseline itself. Instead, the approach suggested in the present work is based on an assumption that the vibrational bands can be "detected" and "masked" by their first and second derivatives of the raw spectrum. This makes it possible to handle the broad tails and superimposed bands very efficiently. The proposed algorithm was compared to several state-of-the-art approaches including original and recently proposed ALS implementations, wavelet-transform, and morphological algorithms. The "quality" of different algorithms was quantitatively estimated by adding random smooth curves to the spectrum. It was found that among the tested approaches, the peak-screened ALS algorithm shows the best separation of the broad superimposed Raman peaks from the smooth random baselines. The Python code of the algorithm is provided.

show abstract

“…The Mexican hat wavelet basis function has advantageous properties of simple and rapid calculation. It also has the advantage of baseline deduction in the ridge spectral peak recognition as the second derivative of the Gaussian density function. , The Mexican hat wavelet basis function was therefore used for CWT of the T 2 distribution in this paper, which can be written as The diagram of the Mexican hat wavelet basis function is shown in Figure . It is symmetrical and can effectively avoid phase distortion in the process of CWT.…”

Section: Theory and Methodsmentioning

confidence: 99%

“…It also has the advantage of baseline deduction in the ridge spectral peak recognition as the second derivative of the Gaussian density function. 27,28 The Mexican hat wavelet basis function was therefore used for CWT of the T 2 distribution in this paper, which can be written as…”

Section: Continuous Wavelet Transform Of the T 2 Distributionmentioning

confidence: 99%

Novel Method for Determining Irreducible Water Saturation in a Tight Sandstone Reservoir Based on the Nuclear Magnetic Resonance T₂ Distribution

et al. 2022

View full text Add to dashboard Cite

Irreducible water saturation, affecting the productivity of a reservoir, is one of the key parameters for a tight sandstone reservoir. Nuclear magnetic resonance (NMR) logging, detecting the relaxation information on pore fluid with hydrogen nuclei in formation, can be used to distinguish irreducible and movable fluids as a result of their different relaxation information. The irreducible water saturation can be determined by the T 2 cutoff method in the conventional reservoir. However, a tight sandstone reservoir possesses a complex pore structure, and relaxation signals of different pore fluids may overlap in the T 2 distribution. The T 2 cutoff method to determine irreducible water saturation may be a challenge in tight sandstone reservoirs. In this study, a novel T 2-distribution-based method, with the film model assumption, was proposed to determine the irreducible water saturation in tight sandstone reservoirs. The T 2 distribution was transformed by a continuous wavelet transform to obtain the two-dimensional matrix of wavelet coefficients, and the position and shape parameters of the spectral peak at different scales in wavelet space were determined. These parameters were used to construct the Gaussian distribution functions (GDFs) at different scales. The T 2 distribution was decomposed on the basis of the constructed GDFs, and then the optimal decomposition was selected. The irreducible water saturation is determined by the optimal T 2 distribution decomposed. The effectiveness and practicability of the proposed method were validated by numerical simulation, core experimental, and NMR logging data processing.

show abstract

Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection

Abstract: Peak detection is a crucial step in spectral signal pre-processing.

Cited by 24 publications

References 26 publications

Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up Areas

Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up Areas

Asymmetric least‐squares baseline algorithm with peak screening for automatic processing of the Raman spectra

Novel Method for Determining Irreducible Water Saturation in a Tight Sandstone Reservoir Based on the Nuclear Magnetic Resonance T₂ Distribution

Contact Info

Product

Resources

About

Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection

Abstract: Peak detection is a crucial step in spectral signal pre-processing.

Cited by 24 publications

References 26 publications

Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up Areas

Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up Areas

Asymmetric least‐squares baseline algorithm with peak screening for automatic processing of the Raman spectra

Novel Method for Determining Irreducible Water Saturation in a Tight Sandstone Reservoir Based on the Nuclear Magnetic Resonance T2 Distribution

Contact Info

Product

Resources

About

Novel Method for Determining Irreducible Water Saturation in a Tight Sandstone Reservoir Based on the Nuclear Magnetic Resonance T₂ Distribution