New method for denoising borehole transient electromagnetic data with discrete wavelet transform

Dai, Xueping; Cheng, Li Zhen; Mareschal, Jean‐Claude; Lemire, Daniel; Liu, Chong

doi:10.1016/j.jappgeo.2019.05.009

Cited by 20 publications

(5 citation statements)

References 8 publications

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“…This mechanism effectively reduces noise in the image and preserves spectral information. SOC changes are non-stationary and non-linear, and wavelet and Fourier transformations have clear advantages in processing non-stationary data [41,42]. Consistent with our research, Meng et al also demonstrated that DWT is an effective method for reducing noise in hyperspectral images [20].…”

Section: Advantages Of Image Denoising In Satellite Hyperspectral Pre...supporting

confidence: 88%

Comparing Laboratory and Satellite Hyperspectral Predictions of Soil Organic Carbon in Farmland

Jin,

Peng,

et al. 2024

Agronomy

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Mapping soil organic carbon (SOC) accurately is essential for sustainable soil resource management. Hyperspectral data, a vital tool for SOC mapping, is obtained through both laboratory and satellite-based sources. While laboratory data is limited to sample point monitoring, satellite hyperspectral imagery covers entire regions, albeit susceptible to external environmental interference. This study, conducted in the Yuncheng Basin of the Yellow River Basin, compared the predictive accuracy of laboratory hyperspectral data (ASD FieldSpec4) and GF-5 satellite hyperspectral imagery for SOC mapping. Leveraging fractional order derivatives (FODs), various denoising methods, feature band selection, and the Random Forest model, the research revealed that laboratory hyperspectral data outperform satellite data in predicting SOC. FOD processing enhanced spectral information, and discrete wavelet transform (DWT) proved effective for GF-5 satellite imagery denoising. Stability competitive adaptive re-weighted sampling (sCARS) emerged as the optimal feature band selection algorithm. The 0.6FOD-sCARS RF model was identified as the optimal laboratory hyperspectral prediction model for SOC, while the 0.8FOD-DWT-sCARS RF model was deemed optimal for satellite hyperspectral prediction. This research, offering insights into farmland soil quality monitoring and strategies for sustainable soil use, holds significance for enhancing agricultural production efficiency.

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Section: Advantages Of Image Denoising In Satellite Hyperspectral Pre...supporting

confidence: 88%

Comparing Laboratory and Satellite Hyperspectral Predictions of Soil Organic Carbon in Farmland

Jin,

Peng,

et al. 2024

Agronomy

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“…FT uses large waves, while DWT uses small waves [71]. Similar to FT's transformation, which of time domain signals into triangle functions, DWT decomposes signals into different scale components [72], and the resolution in the time and frequency domains is linked to the type of base function used for transformation. The resolution expands (or contracts) according to a scale factor and is temporally shifted along the entire definition range of the signal.…”

Section: Comparation On the Performances Of Different Denoising Methodsmentioning

confidence: 99%

Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Meng

Bao

et al. 2021

Remote Sensing

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In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.

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“…The discrete wavelet transform (DWT), which provides a window changing with the frequency to analyze and process the time-frequency signal, can decompose a signal into many scales which ranges from the roughest scale to the finest one [16]. With its outstanding feature of detecting singularities and transients [17], DWT has been widely used in spectra data to realize noise reduction. The process of baseline correcting is as follows: With the ability of loading spectra data, saving, baseline correction, and de-noising, peak detection module can automatically search peak and be matched with NIST database.…”

Section: Peak Detectionmentioning

confidence: 99%

A software system for rapid analysis of plastics using laser-induced breakdown spectroscopy

et al. 2020

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A: With the extensive use of plastic products, the recycling and reuse of plastics raises more concerns. Laser-induced breakdown spectroscopy (LIBS) has become a powerful technology for plastic analysis. To meet different analytical requirements, LIBS control system and analysis software was being continuously developed. However, existing software system only can realize the capability of LIBS system control or data analysis (peak detection or material identification). To improve the efficiency of LIBS instrument for plastic analysis, an integrated and full software is needed. In this work, analyis & test software was developed for rapid plastic analysis, which consists of LIBS control platform, plastic database, and analysis module which contains peak detection, qualitative and quantitative analysis. The software performance was validated, which provides an automatic processing for plastic analysis. Design approach of the software can be used for the development of similar instrument software and other samples analysis. K: Data processing methods; Pattern recognition, cluster finding, calibration and fitting methods; Plasma generation (laser-produced, RF, x ray-produced); Software architectures (event data models, frameworks and databases) 1Corresponding author.

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New method for denoising borehole transient electromagnetic data with discrete wavelet transform

Cited by 20 publications

References 8 publications

Comparing Laboratory and Satellite Hyperspectral Predictions of Soil Organic Carbon in Farmland

Comparing Laboratory and Satellite Hyperspectral Predictions of Soil Organic Carbon in Farmland

Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

A software system for rapid analysis of plastics using laser-induced breakdown spectroscopy

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