Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data

Wang, Wenguang; Ren, Xin; Zhang, Yan; Li, Meng

doi:10.3390/app8091513

Cited by 19 publications

(5 citation statements)

References 19 publications

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“…Previous studies utilizing SAR images to discriminate rock units or lithology usually preferred to use full-polarimetric data such as Radarsat-2 [40] or multi-frequency SAR data for example, shuttle imaging radar (SIC) SAR data [41] or together with other source data such as optical data and elevation data [33]. Very few studies employed the dualpolarization Sentinel-1 SAR data.…”

Section: Discussionmentioning

confidence: 99%

“…Wang et al [40] compared two frequencies (L-band and C-band) in polarimetric SAR images for mapping iron-mineralized laterites, and the L-band provided superior results in comparison to C-band. Dual-frequency polarimetric SAR was used together with a deep learning method to successfully classify lithology [41]. Although the advantages of SAR data have been around for a long time for its imaging capability in moist and vegetated regions for geological survey [42], later research has proven that vegetation and topography can still limit the reliability of SAR in geological interpretation [43].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Performance of Time-Series Sentinel-1 Data for Discriminating Rock Units

2021

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The potential use of time-series Sentinel-1 synthetic aperture radar (SAR) data for rock unit discrimination has never been explored in previous studies. Here, we employed time-series Sentinel-1 data to discriminate Dananhu formation, Xinjiang group, Granite, Wusu group, Xishanyao formation, and Diorite in Xinjiang, China. Firstly, the temporal variation of the backscatter metrics (backscatter coefficient and coherence) from April to October derived from Sentinel-1, was analyzed. Then, the significant differences of the time-series SAR metrics among different rock units were checked using the Kruskal–Wallis rank sum test and Tukey’s honest significant difference test. Finally, random forest models were used to discriminate rock units. As for the input features, there were four groups: (1) time-series backscatter metrics, (2) single-date backscatter metrics, (3) time-series backscatter metrics at VV, and (4) VH channel. In each feature group, there were three sub-groups: backscatter coefficient, coherence, and combined use of backscatter coefficient and coherence. Our results showed that time-series Sentinel-1 data could improve the discrimination accuracy by roughly 9% (from 55.4% to 64.4%), compared to single-date Sentinel-1 data. Both VV and VH polarization provided comparable results. Coherence complements the backscatter coefficient when discriminating rock units. Among the six rock units, the Granite and Xinjiang group can be better differentiated than the other four rock units. Though the result still leaves space for improvement, this study further demonstrates the great potential of time-series Sentinel-1 data for rock unit discrimination.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the Performance of Time-Series Sentinel-1 Data for Discriminating Rock Units

2021

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“…While the X-band (8~12 GHz) might not be commonly used for direct lithology identification in remote sensing applications, it can still contribute to lithology identification efforts by providing valuable information through high-precision topographic data. Sentinel-1 [46], polarimetric SAR (Pol-SAR) [47], and Phased Array type L-band SAR (PALSAR) [45,48] are highly favored for lithological mapping. However, studies have shown that radar data generally has lower spatial resolution compared to optical images, and the acquisition and processing of radar data can be complex [45].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

Chen,

Wang,

Zhang

et al. 2023

Minerals

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Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions which includes the extensively reviewed prior research concerning the identification of lithology in vegetated regions, encompassing the utilized remote sensing data sources, and classification methodologies. Moreover, it offers a comprehensive overview of the application of remote sensing techniques in the domain of lithological mapping. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact the accuracy of lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to improve classification accuracy and the exploration of novel RS techniques and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.

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“…Currently, methods to improve the accuracy of rock-type discrimination based on SAR data focus on (i) improvements in classification algorithms (e.g., improvements in deeplearning algorithms) [15], (ii) using time series to filter features [16], and (iii) using multifrequency or fully polarized data [15,17]. With the development of artificial intelligence, the classification ability of classifiers has gradually improved.…”

Section: Introductionmentioning

confidence: 99%

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

Guo

Yang

et al. 2022

Remote Sensing

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Lithological mapping using dual-polarization synthetic aperture radar (SAR) data is limited by the low classification accuracy. In this study, we extract ten parameters (backscatter coefficients and polarization decomposition parameters) from the Sentinel-1 dual-pol SAR data. Using 94 mother wavelet functions (MF), a one-level two-dimensional discrete wavelet transform (DWT) is applied to all the parameters, and the suitable MF is screened by comparing the overall accuracy and F1 score. Finally, the lithological mapping of the study area is performed. According to the cross-validation results, DWT can improve the overall accuracy for all MF. Db13 improved the overall accuracy by 6.1% (from 49.5% to 55.6%). The F1 score of granitoids improved by 0.223. Among the five rock units, Grantoids and Quaternary alluvium and sediment with finer gravel can be better differentiated than the other three rock units. The overall accuracy of effusive rocks (marine basic volcanic rocks) is not improved by DWT, but this study confirms the great potential of DWT in lithology classification.

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Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data

Cited by 19 publications

References 19 publications

Evaluation of the Performance of Time-Series Sentinel-1 Data for Discriminating Rock Units

Evaluation of the Performance of Time-Series Sentinel-1 Data for Discriminating Rock Units

Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

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

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