Retraction Note: Novel Rock Image Classification: The Proposal and Implementation of RockNet

Zhou, Yimeng; Wong, Louis Ngai Yuen; Tse, Keith Ki Chun

doi:10.1007/s00603-023-03254-x

Cited by 2 publications

(2 citation statements)

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“…In contrast, transfer learning methods do not necessitate a specific distribution of training and test samples, enabling the reuse of samples [23][24][25][26][27]. Transfer learning methods can give full play to the values of ground-measured samples and realize the transfer application in temporal and cross-region remote sensing images [31][32][33][34]. Therefore, to reduce the dependence on the ground samples, it is valuable to evaluate the potential of transfer learning methods for mapping forest AGB in the homogeneous forest using the same type of remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Transferability of Spectral Variables and Prediction Models for Mapping Forest Aboveground Biomass Using Transfer Learning Methods

Chen,

Lin,

Long

et al. 2023

Remote Sensing

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Forests, commonly viewed as the Earth’s lungs, play a crucial role in mitigating greenhouse gas emissions, regulating the globe, and maintaining ecological equilibrium. The assessment of aboveground biomass (AGB) serves as a pivotal indicator for evaluating forest quality. By integrating remote sensing images with a small number of ground-measured samples to map, forest AGBs can significantly reduce time and labor costs. Current research mainly focuses on improving the accuracy of mapping forest AGBs, such as integrating multiple-sensors remote sensing data and models. However, due to uncertainties associated with remote sensing images and complexities inherent in forest structures, the accuracy of mapping forest AGBs is constrained by both the quantity and distribution of ground samples available. The development of transfer learning methods can fully utilize ground-based measurement data and enable the application of samples across regions and time. To evaluate the potential of transfer learning methods in mapping forest AGBs, this study conducted a spatial–temporal transfer of spectral variables (SVs) and prediction models (PMs) using a direct-push transfer method, and a new evaluation metric, relative change of R-squared (RCRS), was proposed to assess the transferability of SVs and PMs. The results showed that the transferability of SVs and PMs in the spatial target domain is obviously greater than that in the temporal target domain. Compared to the temporal target domain, the RCRS for transfer SVs in the spatial target domain was lower by 20.89 (oak) and 20.88 (Chinese fir) and for transfer PMs by 24.16 (oak) and 24.79 (Chinese fir). Tree species is also one of the main factors affecting the spatial and temporal transfer of SVs, and it is challenging to transfer SVs between different tree species. The results also show that nonparametric models have better generalization performance, and their transferability is much greater than that of parametric models.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Transferability of Spectral Variables and Prediction Models for Mapping Forest Aboveground Biomass Using Transfer Learning Methods

Chen,

Lin,

Long

et al. 2023

Remote Sensing

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“…To overcome the restriction of the prediction precision of RA, this study employed CNN to predict the UCS of sandy dolomite. The CNN originated in the 1980s (Rumelhart et al, 1986), and then has been widely applied, i.e., civil and mining engineering and detection (Huang et al, 2018a;Karimpouli et al, 2022;Zhang et al, 2019;Zhang et al, 2020), rock properties (physico-mechanical properties, chemical compositions, permeability, porosity, rock mass strength, macro and micro image recognition) (Alzubaidi et al, 2022;Bergen et al, 2019;Chen et al, 2021a;Chen et al, 2021b;Ferreira and Giraldi, 2017;Huang et al, 2018b;Karimpouli and Tahmasebi, 2019;Niu et al, 2020;Sidorenko et al, 2021;Tang et al, 2022;Tian et al, 2020;Wang et al, 2021;Wu et al, 2018;Zhou et al, 2022). The CNN-based analysis method for the prediction of the UCS of sandy dolomite has not been reported yet.…”

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confidence: 99%

Comparative Study on Convolutional Neural Network and Regression Analysis to Evaluate Uniaxial Compressive Strength of Sandy Dolomite

Xu,

Wang,

Liu

et al. 2024

Preprint

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Sandy dolomite is a kind of widely distributed rock. The uniaxial compressive strength (UCS) of sandy dolomite is an important metric in the application in civil engineering, geotechnical engineering, and underground engineering. Direct measurement of UCS is costly, time-consuming, and even infeasible in some cases. To address this problem, we establish an indirect measuring method based on the convolutional neural network (CNN) and regression analysis (RA). The new method is straightforward and effective for UCS prediction, and has significant practical implications. To evaluate the performance of the new method, 158 dolomite samples of different sandification grades are collected for testing their UCS along and near the Yuxi section of the Central Yunnan Water Diversion (CYWD) Project in Yunnan Province, Southwest of China. Two regression equations with high correlation coefficients are established according to the RA results, to predict the UCS of sandy dolomites. Moreover, the minimum thickness of sandy dolomite was determined by the Schmidt hammer rebound test. Results show that CNN outperforms RA in terms of prediction the precision of sandy dolomite UCS. In addition, CNN can effectively deal with uncertainty in test results, making it one of the most effective tools for predicting the UCS of sandy dolomite.

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Retraction Note: Novel Rock Image Classification: The Proposal and Implementation of RockNet

Cited by 2 publications

References 0 publications

Evaluating the Transferability of Spectral Variables and Prediction Models for Mapping Forest Aboveground Biomass Using Transfer Learning Methods

Evaluating the Transferability of Spectral Variables and Prediction Models for Mapping Forest Aboveground Biomass Using Transfer Learning Methods

Comparative Study on Convolutional Neural Network and Regression Analysis to Evaluate Uniaxial Compressive Strength of Sandy Dolomite

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