Improvement of Lithological Mapping Using Discrete Wavelet Transformation from Sentinel-1 SAR Data

Guo, Senmiao; Yang, Changbao; He, Rizheng; Li, Yanqi

doi:10.3390/rs14225824

Cited by 9 publications

(15 citation statements)

References 44 publications

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“…However, caution should be exercised when fine-tuning parameters for optimal outcomes and effectively managing computational expenses, particularly when dealing with substantial datasets. The risk of overfitting due to an abundance of trees or noisy data should be considered, along with its limited efficacy with imbalanced datasets (Guo et al, 2022).…”

Section: Classification Algorithms For Lithological Mappingmentioning

confidence: 99%

Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas

Chen,

Dong,

Wang

et al. 2023

Front. Ecol. Evol.

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Lithological mapping in highly vegetated areas using remote sensing techniques poses a significant challenge. Inspired by the concept of “geobotany”, we attempted to distinguish lithologies indirectly using machine learning algorithms (MLAs) based on Sentinel-2 and SRTM DEM in Zhangzhou City, Fujian Province. The study area has high vegetation cover, with lithologies that are largely obscured. After preprocessing such as cloud masking, resampling, and median image synthesis, 17 spectral bands and features from Sentinel-2 and 9 terrain features from DEM were extracted. Five widely used MLAs, MD, CART, SVM, RF, and GBDT, were trained and validated for lithological mapping. The results indicate that advanced MLAs, such as GBDT and RF, are highly effective for nonlinear modeling and learning with relative increases reaching 8.18%∼11.82% for GBDT and 6.36%∼10% for RF. Compared with optical imagery or terrain data alone, combining Sentinel-2 and DEM significantly improves the accuracy of lithological mapping, as it provides more comprehensive and precise spectral characteristics and spatial information. GBDT_Sen+DEM utilizing integrated data achieved the highest classification accuracy, with an overall accuracy of 63.18%. This study provides a case study for lithological mapping of areas with high vegetation cover at the local level. This also reinforces the idea that merging remote sensing and terrain data significantly enhances the precision and reliability of the lithological mapping methods.

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Section: Classification Algorithms For Lithological Mappingmentioning

confidence: 99%

Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas

Chen,

Dong,

Wang

et al. 2023

Front. Ecol. Evol.

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“…2,38,39 The performance of Sentinel-1A (S-1A) data was assessed for lithology classification using discrete wavelet transformation and implementing H/A/Alpha decomposition. 40 Apart from the use of C-band SAR, L-band ALOS PALSAR (AP) images have also proven to be useful for geological investigations due to their high penetration capability compared with C-band SAR images. 41 Fully polarimetric SAR (AP) data have also been evaluated for lithology identification and shown to obtain better accuracy in lithology classification using the Cloude-Pottier decomposition and support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the detailed analysis of varying backscatter returns from different rocks, and the textural information extracted for SAR images shows a great potential in different geological investigations 2 , 38 , 39 . The performance of Sentinel-1A (S-1A) data was assessed for lithology classification using discrete wavelet transformation and implementing H/A/Alpha decomposition 40 . Apart from the use of C-band SAR, L-band ALOS PALSAR (AP) images have also proven to be useful for geological investigations due to their high penetration capability compared with C-band SAR images 41 .…”

Section: Introductionmentioning

confidence: 99%

Discernment of complex lithologies utilizing different scattering and textural components of SAR and optical data through machine learning approaches in Jaisalmer, Rajasthan, India

Biswas,

Rathore

2023

J. Appl. Rem. Sens.

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.Accurate lithological mapping is a difficult task through standard image processing techniques. We utilize the application of different machine learning (ML) algorithms on dual polarimetric synthetic aperture radar (SAR), optical data, and surface elevation images to map various lithologies in parts of Jaisalmer district of Rajasthan, India. Different SAR-derived textural and decomposition parameters were also used to improve the discrimination of various lithology units. Further, to improve the classification accuracy, different ML-based feature importance models, such as XGboost, decision tree, and random forest were implemented to select the useful bands for the classification of lithology. A total of 14 different ML classifiers were applied, and the best classifier was chosen after comparing their accuracies (overall accuracy, kappa coefficient, F1 score, and ROC-AUC curve) to map the lithology. Out of all of the classifiers used in this study, light gradient boosting machine (lightgbm) performed better in discriminating lithology (OA = 0.80, kappa coefficient = 0.75, and F1 score 0.79). In addition, the AUC values (>0.9 in all lithology units) were obtained for the “lightgbm” model, which is indicative of accurate discrimination of different lithological classes.

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“…Although rock spectra have high intra-class variability, spectral curves of the same type of rock may have similar absorption characteristics [30,31]. Wavelet transform has advantages in extracting subtle features [32,33]. Chen et al [34] proposed an adaptive wavelet filter for image denoising, which can effectively reduce the variable band noise of different targets.…”

Section: Introductionmentioning

confidence: 99%

Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data

Guo,

Jiang

2023

Remote Sensing

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The high intra-class variability of rock spectra is an important factor affecting classification accuracy. The discrete wavelet transform (DWT) can capture abrupt changes in the signal and obtain subtle differences between the spectra of different rocks. Taking laboratory spectra and hyperspectral data as examples, high-frequency features after DWT were used to improve the discrimination accuracy of rocks. Various decomposition levels, mother wavelet functions, and reconstruction methods were used to compare the accuracy. The intra-class variability was measured using the intra-class Spectral Angle Mapper (SAM). Our results show that the high-frequency features could improve the discrimination accuracy of laboratory spectra by 13.4% (from 46.5% to 59.9%), compared to the original spectral features. The accuracy of image spectra in two study areas increased by 8.6% (from 68.3% to 76.9%) and 7.2% (from 81.3% to 88.5%), respectively. Haar wavelets highlighted the spectral differences between different rocks. After DWT, intra-class SAM reduced and intra-class variability of rocks decreased. The Pearson correlation coefficient indicated a negative correlation between intra-class variability and overall accuracy. It suggested that improving classification accuracy by reducing intra-class variability was feasible. Though the result of lithological mapping still leaves room for improvement, this study provides a new approach to reduce intra-class variability, whether using laboratory spectra or hyperspectral data.

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Improvement of Lithological Mapping Using Discrete Wavelet Transformation from Sentinel-1 SAR Data

Cited by 9 publications

References 44 publications

Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas

Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas

Discernment of complex lithologies utilizing different scattering and textural components of SAR and optical data through machine learning approaches in Jaisalmer, Rajasthan, India

Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data

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