In this paper,we introduce an accelerating algorithm based on the Taylor series for reconstructing target images in the spectral digital image correlation method(SDIC).The Taylor series image reconstruction method is ...In this paper,we introduce an accelerating algorithm based on the Taylor series for reconstructing target images in the spectral digital image correlation method(SDIC).The Taylor series image reconstruction method is employed instead of the previous direct Fourier transform(DFT)image reconstruction method,which consumes the majority of the computational time for target image reconstruction.The partial derivatives in the Taylor series are computed using the fast Fourier transform(FFT)of the entire image,following the principles of Fourier transform theory.To examine the impact of different orders of Taylor series expansion on accuracy and efficiency,we employ third-and fourth-order Taylor series image reconstruction methods and compare them with the DFT image reconstruction method through simulated experiments.As a result of these enhancements,the computational efficiency using the third-and fourth-order Taylor series improves by factors of 57 and 46,respectively,compared to the previous method.In terms of analysis accuracy,within a strain range of 0–0.1 and without the addition of image noise,the accuracy of the proposed method increases with higher expansion orders,surpassing that of the DFT image reconstruction method when the fourth order is utilized.However,when different levels of Gaussian noise are applied to simulated images individually,the accuracy of the third-or fourth-order Taylor series expansion method is superior to that of the DFT reconstruction method.Finally,we present the analyzed experimental results of a silicone rubber plate specimen with bilateral cracks under uniaxial tension.展开更多
Historic maps showing the temporal distribution of rice fields are important for precision agriculture,irrigation optimisation,forecasting crop yields,land use management and formulating policies.However,mapping rice ...Historic maps showing the temporal distribution of rice fields are important for precision agriculture,irrigation optimisation,forecasting crop yields,land use management and formulating policies.However,mapping rice felds using traditional ground surveys is impractical when high cost,time and labour requirements are considered,and the availability of such detailed records is limited.Although satellite remote sensing appears to be a viable solution,conventional segmentation and classification methods with spectral bands are often unable to contrast the distinct characteristics between rice fields and other vegetation classes.To this end,we explored a novel,Google Earth Engine(GEE)based multiindex random forest(RF)classification approach to map rice fields over two decades.Landsat images from 2000 to 2020 of two Sri Lankan rice cultivation districts were extracted from GEE and a multi-index RF classification algorithm was applied to distinguish the rice fields.The results showed above 80%accuracy for both training and validation,when compared against high spatial resolution Google Earth imagery.In essence,multi-index sampling and RF together synergised the compelling classifcation accuracy by effectively capturing vegetation,water(ponding)and soil characteristics unique to the rice felds using a single-click approach.The maps developed in this study were further compared against the MODIS land cover type product(MCD12Q1)and the corresponding superior statistics on rice fields demonstrated the robustness of the proposed approach.Future work seeking effective index combinations is recommended,and this approach can potentially be extended to other crop analyses elsewhere.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12272145 and 11972013)the Ministry of Science and Technology of China(Grant No.2018YFF01014200)Hubei Provincial Natural Science Foundation of China(Grant No.2022CFB288).
文摘In this paper,we introduce an accelerating algorithm based on the Taylor series for reconstructing target images in the spectral digital image correlation method(SDIC).The Taylor series image reconstruction method is employed instead of the previous direct Fourier transform(DFT)image reconstruction method,which consumes the majority of the computational time for target image reconstruction.The partial derivatives in the Taylor series are computed using the fast Fourier transform(FFT)of the entire image,following the principles of Fourier transform theory.To examine the impact of different orders of Taylor series expansion on accuracy and efficiency,we employ third-and fourth-order Taylor series image reconstruction methods and compare them with the DFT image reconstruction method through simulated experiments.As a result of these enhancements,the computational efficiency using the third-and fourth-order Taylor series improves by factors of 57 and 46,respectively,compared to the previous method.In terms of analysis accuracy,within a strain range of 0–0.1 and without the addition of image noise,the accuracy of the proposed method increases with higher expansion orders,surpassing that of the DFT image reconstruction method when the fourth order is utilized.However,when different levels of Gaussian noise are applied to simulated images individually,the accuracy of the third-or fourth-order Taylor series expansion method is superior to that of the DFT reconstruction method.Finally,we present the analyzed experimental results of a silicone rubber plate specimen with bilateral cracks under uniaxial tension.
文摘Historic maps showing the temporal distribution of rice fields are important for precision agriculture,irrigation optimisation,forecasting crop yields,land use management and formulating policies.However,mapping rice felds using traditional ground surveys is impractical when high cost,time and labour requirements are considered,and the availability of such detailed records is limited.Although satellite remote sensing appears to be a viable solution,conventional segmentation and classification methods with spectral bands are often unable to contrast the distinct characteristics between rice fields and other vegetation classes.To this end,we explored a novel,Google Earth Engine(GEE)based multiindex random forest(RF)classification approach to map rice fields over two decades.Landsat images from 2000 to 2020 of two Sri Lankan rice cultivation districts were extracted from GEE and a multi-index RF classification algorithm was applied to distinguish the rice fields.The results showed above 80%accuracy for both training and validation,when compared against high spatial resolution Google Earth imagery.In essence,multi-index sampling and RF together synergised the compelling classifcation accuracy by effectively capturing vegetation,water(ponding)and soil characteristics unique to the rice felds using a single-click approach.The maps developed in this study were further compared against the MODIS land cover type product(MCD12Q1)and the corresponding superior statistics on rice fields demonstrated the robustness of the proposed approach.Future work seeking effective index combinations is recommended,and this approach can potentially be extended to other crop analyses elsewhere.
