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Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning 被引量:1
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作者 Hui-Ting Ge Jian-Wu Chen +5 位作者 Li-Li Wang Tian-Xiu Zou Bin Zheng Yuan-Fen Liu Yun-Jing Xue Wei-Wen Lin 《World Journal of Gastroenterology》 SCIE CAS 2024年第6期542-555,共14页
BACKGROUND Lymphovascular invasion(LVI)and perineural invasion(PNI)are important prognostic factors for gastric cancer(GC)that indicate an increased risk of metastasis and poor outcomes.Accurate preoperative predictio... BACKGROUND Lymphovascular invasion(LVI)and perineural invasion(PNI)are important prognostic factors for gastric cancer(GC)that indicate an increased risk of metastasis and poor outcomes.Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment deci-sions.However,prior models using conventional computed tomography(CT)images to predict LVI or PNI separately have had limited accuracy.Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion.We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients.AIM To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately.METHODS This study used a retrospective dataset involving 257 GC patients(training cohort,n=172;validation cohort,n=85).First,several clinical indicators,including serum tumor markers,CT-TN stages and CT-detected extramural vein invasion(CT-EMVI),were extracted,as were quantitative spectral CT parameters from the delineated tumor regions.Next,a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters.A logistic regression(LR)-based nomogram model was subsequently constructed to predict LVI/PNI status,and its performance was evaluated using the area under the receiver operating characteristic curve(AUC).RESULTS In both the training and validation cohorts,CT T3-4 stage,CT-N positive status,and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant(P<0.05).LR analysis of the training group showed preoperative CT-T stage,CT-EMVI,single-energy CT values of 70 keV of venous phase(VP-70 keV),and the ratio of standardized iodine concentration of equilibrium phase(EP-NIC)were independent influencing factors.The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824,respectively,which were slightly greater than those of CT-T and CT-EMVI(AUC=0.793,0.762).The nomogram combining CT-T stage,CT-EMVI,VP-70 keV and EP-NIC yielded AUCs of 0.918(0.866-0.954)and 0.874(0.784-0.936)in the training and validation cohorts,which are significantly higher than using each of single independent factors(P<0.05).CONCLUSION The study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC,with accuracy boosted by integrating clinical markers. 展开更多
关键词 spectral computed tomography Gastric cancer Lymphovascular invasion Perineural invasion
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Experimental investigation of spectral evolution in flash radiation by hypervelocity impact on aluminum plates
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作者 Xing Chen Yonggang Lu +1 位作者 Zhiwen Li Zhonghua Cui 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第7期96-110,共15页
In this study,a series of hypervelocity impact tests were carried out based on a two-stage light gas gun,and the sequence spectrum and radiation evolution data of the impact products under different impact conditions ... In this study,a series of hypervelocity impact tests were carried out based on a two-stage light gas gun,and the sequence spectrum and radiation evolution data of the impact products under different impact conditions were obtained.The diameter of the projectile is 3-5 mm,the impact velocity is 3.13-6.58 km/s,and the chamber pressure is 0.56-990 Pa.The spectrum of ejected debris cloud in the 250-310 nm band were obtained using a transient spectral measurement system and a multi-channel radiometer measurement system.The test results reveal that the flash radiation intensity increases as a power function with the kinetic energy of the impact.Furthermore,the peak value of the line spectrum decreases as the chamber vacuum degree increases,while the radiation width gradually expands.The line spectrum in the spectral characterization curve corresponds to the ejected debris clouds splitting phase,which does not produce significant line spectrum during material fragmentation and is dominated by the continuum spectrum produced by blackbody radiation.There will appear one or three characteristic peaks in the flash radiation time curve,the first and second peaks correspond to the penetration phase and the third peak corresponds to the expansion phase of the ejected debris clouds on the time scale,the first and second peaks are more sensitive to the chamber vacuum degree,and when the pressure is higher than 99 Pa,the first and second characteristic peaks will disappear.The radiant heat attenuation of the flash under different impact conditions is significantly different,the attenuation exponent has a power function relationship with the impact velocity and the chamber vacuum degree,while the attenuation exponent has a linear relationship with the diameter of the projectile,the specific expression of the attenuation exponent is obtained by fitting.The findings from this research can serve as a valuable reference for remote diagnostic technologies based on flash radiation characteristics. 展开更多
关键词 Hypervelocity impact Flash radiation EVOLUTION spectral characteristics Damage evaluation
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S^(2)ANet:Combining local spectral and spatial point grouping for point cloud processing
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作者 Yujie LIU Xiaorui SUN +1 位作者 Wenbin SHAO Yafu YUAN 《虚拟现实与智能硬件(中英文)》 EI 2024年第4期267-279,共13页
Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider ... Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider only the spatial domain in the feature extraction process.Methods In this paper,we propose a spectral and spatial aggregation convolutional network(S^(2)ANet),which combines spectral and spatial features for point cloud processing.First,we calculate the local frequency of the point cloud in the spectral domain.Then,we use the local frequency to group points and provide a spectral aggregation convolution module to extract the features of the points grouped by the local frequency.We simultaneously extract the local features in the spatial domain to supplement the final features.Results S^(2)ANet was applied in several point cloud analysis tasks;it achieved stateof-the-art classification accuracies of 93.8%,88.0%,and 83.1%on the ModelNet40,ShapeNetCore,and ScanObjectNN datasets,respectively.For indoor scene segmentation,training and testing were performed on the S3DIS dataset,and the mean intersection over union was 62.4%.Conclusions The proposed S^(2)ANet can effectively capture the local geometric information of point clouds,thereby improving accuracy on various tasks. 展开更多
关键词 Local frequency spectral and spatial aggregation convolution spectral group convolution Point cloud representation learning Graph convolutional network
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Decoupling of temporal/spatial broadening effects in Doppler wind LiDAR by 2D spectral analysis
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作者 刘珍 张云鹏 +3 位作者 竹孝鹏 刘继桥 毕德仓 陈卫标 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第3期447-452,共6页
Pulse echo accumulation is commonly employed in coherent Doppler wind LiDAR(light detection and ranging)under the assumption of steady wind.Here,the measured spectral data are analyzed in the time dimension and freque... Pulse echo accumulation is commonly employed in coherent Doppler wind LiDAR(light detection and ranging)under the assumption of steady wind.Here,the measured spectral data are analyzed in the time dimension and frequency dimension to cope with the temporal wind shear and achieve the optimal accumulation time.A hardware-efficient algorithm combining the interpolation and cross-correlation is used to enhance the wind retrieval accuracy by reducing the frequency sampling interval and then reduce the spectral width calculation error.Moreover,the temporal broadening effect and spatial broadening effect are decoupled according to the strategy we developed. 展开更多
关键词 Doppler wind LiDAR spectral analysis hardware efficiency spectrum broadening effects
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Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning
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作者 Haiyu Tao Ruiheng Zhou +6 位作者 Yining Tang Wanyu Li Xia Yao Tao Cheng Yan Zhu Weixing Cao Yongchao Tian 《The Crop Journal》 SCIE CSCD 2024年第3期927-937,共11页
The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering s... The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection. 展开更多
关键词 Wheat spike photosynthesis Yield-related phenotypic trait spectral indices Machine learning Estimation
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Whole-volume histogram analysis of spectral-computed tomography iodine maps characterizes HER2 expression in gastric cancer
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作者 Wei-Ling Zhang Jing Sun +8 位作者 Rong-Fang Huang Yi Zeng Shu Chen Xiao-Peng Wang Jin-Hu Chen Yun-Bin Chen Chun-Su Zhu Zai-Sheng Ye You-Ping Xiao 《World Journal of Gastroenterology》 SCIE CAS 2024年第38期4211-4220,共10页
BACKGROUND Although surgery remains the primary treatment for gastric cancer(GC),the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance.HER2 overexpres... BACKGROUND Although surgery remains the primary treatment for gastric cancer(GC),the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance.HER2 overexpression occurs in approximately 15%-20%of advanced GC cases,directly affecting treatment-related decisions.Spectral-computed tomography(sCT)enables the quantification of material compositions,and sCT iodine concentration parameters have been demonstrated to be useful for the diagnosis of GC and prediction of its invasion depth,angioge-nesis,and response to systemic chemotherapy.No existing report describes the prediction of GC HER2 status through histogram analysis based on sCT iodine maps(IMs).AIM To investigate whether whole-volume histogram analysis of sCT IMs enables the prediction of the GC HER2 status.METHODS This study was performed with data from 101 patients with pathologically confirmed GC who underwent preoperative sCT examinations.Nineteen parameters were extracted via sCT IM histogram analysis:The minimum,maximum,mean,standard deviation,variance,coefficient of variation,skewness,kurtosis,entropy,percentiles(1st,5th,10th,25th,50th,75th,90th,95th,and 99th),and lesion volume.Spearman correlations of the parameters with the HER2 status and clinicopathological parameters were assessed.Receiver operating characteristic curves were used to evaluate the parameters’diagnostic performance.RESULTS Values for the histogram parameters of the maximum,mean,standard deviation,variance,entropy,and percentiles were significantly lower in the HER2+group than in the HER2–group(all P<0.05).The GC differentiation and Lauren classification correlated significantly with the HER2 status of tumor tissue(P=0.001 and 0.023,respectively).The 99th percentile had the largest area under the curve for GC HER2 status identification(0.740),with 76.2%,sensitivity,65.0%specificity,and 67.3%accuracy.All sCT IM histogram parameters correlated positively with the GC HER2 status(r=0.237-0.337,P=0.001-0.017).CONCLUSION Whole-lesion histogram parameters derived from sCT IM analysis,and especially the 99th percentile,can serve as imaging biomarkers of HER2 overexpression in GC. 展开更多
关键词 Gastric cancer spectral computed tomography Iodine map Histogram analysis
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Comparison of a Spectral Bin and Two Multi-Moment Bulk Microphysics Schemes for Supercell Simulation:Investigation into Key Processes Responsible for Hydrometeor Distributions and Precipitation
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作者 Marcus JOHNSON Ming XUE Youngsun JUNG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第5期784-800,共17页
There are more uncertainties with ice hydrometeor representations and related processes than liquid hydrometeors within microphysics parameterization(MP)schemes because of their complicated geometries and physical pro... There are more uncertainties with ice hydrometeor representations and related processes than liquid hydrometeors within microphysics parameterization(MP)schemes because of their complicated geometries and physical properties.Idealized supercell simulations are produced using the WRF model coupled with“full”Hebrew University spectral bin MP(HU-SBM),and NSSL and Thompson bulk MP(BMP)schemes.HU-SBM downdrafts are typically weaker than those of the NSSL and Thompson simulations,accompanied by less rain evaporation.HU-SBM produces more cloud ice(plates),graupel,and hail than the BMPs,yet precipitates less at the surface.The limiting mass bins(and subsequently,particle size)of rimed ice in HU-SBM and slower rimed ice fall speeds lead to smaller melting-level net rimed ice fluxes than those of the BMPs.Aggregation from plates in HU-SBM,together with snow–graupel collisions,leads to a greater snow contribution to rain than those of the BMPs.Replacing HU-SBM’s fall speeds using the formulations of the BMPs after aggregating the discrete bin values to mass mixing ratios and total number concentrations increases net rain and rimed ice fluxes.Still,they are smaller in magnitude than bulk rain,NSSL hail,and Thompson graupel net fluxes near the surface.Conversely,the melting-layer net rimed ice fluxes are reduced when the fall speeds for the NSSL and Thompson simulations are calculated using HU-SBM fall speed formulations after discretizing the bulk particle size distributions(PSDs)into spectral bins.The results highlight precipitation sensitivity to storm dynamics,fall speed,hydrometeor evolution governed by process rates,and MP PSD design. 展开更多
关键词 PRECIPITATION spectral bin microphysics bulk microphysics parameterization microphysics processes WRF model supercell storm
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A research on the effect of plasma spectrum collection device on LIBS spectral intensity
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作者 林晓梅 董艳杰 +5 位作者 林京君 黄玉涛 杨江飞 岳星宇 张倬嘉 段鑫杨 《Plasma Science and Technology》 SCIE EI CAS CSCD 2024年第5期121-128,共8页
Only a small amount of spectral information is collected because the collection solid angle of the optical fiber probe and lens is very limited when collecting spectral information.To overcome this limitation,this stu... Only a small amount of spectral information is collected because the collection solid angle of the optical fiber probe and lens is very limited when collecting spectral information.To overcome this limitation,this study presents a novel method for acquiring plasma spectral information from various spatial directions.A parabolic-shaped plasma spectral collection device(PSCD)is employed to effectively collect more spectral information into the spectrometer,thereby enhancing the overall spectral intensity.The research objects in this study were soil samples containing different concentrations of heavy metals Pb,Cr,and Cd.The results indicate that the PSCD significantly enhances the spectral signal,with an enhancement rate of up to 45%.Moreover,the signal-to-noise ratio also increases by as much as 36%.Simultaneously,when compared to the absence of a device,it is found that there is no significant variation in plasma temperature when the PSCD is utilized.This observation eliminates the impact of the spatial effect caused by the PSCD on the spectral intensity.Consequently,a concentrationspectral intensity relationship curve is established under the PSCD.The results revealed that the linear fitting R^(2)for Pb,Cr,and Cd increased by 0.011,0.001,and 0.054,respectively.Additionally,the limit of detection(LOD)decreased by 0.361 ppm,0.901 ppm,and 0.602 ppm,respectively.These findings indicate that the spectral enhancement rate elevates with the increase in heavy metal concentration.Hence,the PSCD can effectively enhance the spectral intensity and reduce the detection limit of heavy metals in soil. 展开更多
关键词 LIBS plasma spectrum collection device spectral enhancement plasma temperature limit of detection
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Review on article of preoperative prediction in chronic hepatitis B virus patients using spectral computed tomography and machine learning
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作者 Yao-Qian Yuan Qian-Qian Chen 《World Journal of Gastroenterology》 SCIE CAS 2024年第38期4239-4241,共3页
This letter comments on the article that developed and tested a machine learning model that predicts lymphovascular invasion/perineural invasion status by combining clinical indications and spectral computed tomograph... This letter comments on the article that developed and tested a machine learning model that predicts lymphovascular invasion/perineural invasion status by combining clinical indications and spectral computed tomography characteristics accurately.We review the research content,methodology,conclusions,strengths and weaknesses of the study,and introduce follow-up research to this work. 展开更多
关键词 Gastric cancer spectral computed tomography Perineural invasion Lympho-vascular invasion Machine learning
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System error iterative identification for underwater positioning based on spectral clustering
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作者 LU Yu WANG Jiongqi +3 位作者 HE Zhangming ZHOU Haiyin XING Yao ZHOU Xuanying 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第4期1028-1041,共14页
The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by consideri... The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by considering the underwater tar-get as a mass point,as well as the observation system error,the traditional error model best estimation trajectory(EMBET)with little observed data and too many parameters can lead to the ill-condition of the parameter model.In this paper,a multi-station fusion system error model based on the optimal polynomial con-straint is constructed,and the corresponding observation sys-tem error identification based on improved spectral clustering is designed.Firstly,the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization.Then a multi-sta-tion non-oriented graph network is established,which can address the problem of the inaccurate identification for the sys-tem errors.Moreover,the similarity matrix of the spectral cluster-ing is improved,and the iterative identification for the system errors based on the improved spectral clustering is proposed.Finally,the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accu-rately identify the system errors,and moreover can improve the positioning accuracy for the underwater target positioning. 展开更多
关键词 acoustic positioning reduced parameter system error identification improved spectral clustering accuracy analy-sis
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DiriNet:An Estimation Network for Spectral Response Function and Point Spread Function
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作者 Ting Hu Siyuan Cheng Chang Liu 《Journal of Beijing Institute of Technology》 EI CAS 2024年第4期287-297,共11页
Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread function.However,few wo... Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread function.However,few works have been payed on the estimation of the two degra-dation functions.To learn the two functions from image pairs to be fused,we propose a Dirichletnetwork,where both functions are properly constrained.Specifically,the spatial response function isconstrained with positivity,while the Dirichlet distribution along with a total variation is imposedon the point spread function.To the best of our knowledge,the neural network and the Dirichlet regularization are exclusively investigated,for the first time,to estimate the degradation functions.Both image degradation and fusion experiments demonstrate the effectiveness and superiority of theproposed Dirichlet network. 展开更多
关键词 Dirichlet network point spread function spectral response function hyper-spectralimage multi-spectral image
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Efficient simulation of spatially correlated non-stationary ground motions by wavelet-packet algorithm and spectral representation method
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作者 Ji Kun Cao Xuyang +1 位作者 Wang Suyang Wen Ruizhi 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2024年第4期799-814,共16页
Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic ... Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic vector process in practice.The first problem is the inherent limitation and inflexibility of the deterministic time/frequency modulation function.Another difficulty is the estimation of evolutionary power spectral density(EPSD)with quite a few samples.To tackle these problems,the wavelet packet transform(WPT)algorithm is utilized to build a time-varying spectrum of seed recording which describes the energy distribution in the time-frequency domain.The time-varying spectrum is proven to preserve the time and frequency marginal property as theoretical EPSD will do for the stationary process.For the simulation of spatially varying ground motions,the auto-EPSD for all locations is directly estimated using the time-varying spectrum of seed recording rather than matching predefined EPSD models.Then the constructed spectral matrix is incorporated in SRM to simulate spatially varying non-stationary ground motions using efficient Cholesky decomposition techniques.In addition to a good match with the target coherency model,two numerical examples indicate that the generated time histories retain the physical properties of the prescribed seed recording,including waveform,temporal/spectral non-stationarity,normalized energy buildup,and significant duration. 展开更多
关键词 non-stationarity time-varying spectrum wavelet packet transform(WPT) spectral representation method(SRM) response spectrum spatially varying recordings
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Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China
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作者 ZHANG Sumei ZHANG Yuan ZHAO Hongmei 《Chinese Geographical Science》 SCIE CSCD 2024年第3期548-563,共16页
The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate ... The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data. 展开更多
关键词 crop residue burning burned area Sentinel-2 Multi spectral Instrument(MSI) logistic regression Songnen Plain China
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Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network
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作者 Ning ZHANG Junfang YANG +2 位作者 Shanwei LIU Yi MA Jie ZHANG 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2024年第3期728-743,共16页
The accurate identification of marine oil spills and their emulsions is of great significance for emergency response to oil spill pollution.The selection of characteristic bands with strong separability helps to reali... The accurate identification of marine oil spills and their emulsions is of great significance for emergency response to oil spill pollution.The selection of characteristic bands with strong separability helps to realize the rapid calculation of data on aircraft or in orbit,which will improve the timeliness of oil spill emergency monitoring.At the same time,the combination of spectral and spatial features can improve the accuracy of oil spill monitoring.Two ground-based experiments were designed to collect measured airborne hyperspectral data of crude oil and its emulsions,for which the multiscale superpixel level group clustering framework(MSGCF)was used to select spectral feature bands with strong separability.In addition,the double-branch dual-attention(DBDA)model was applied to identify crude oil and its emulsions.Compared with the recognition results based on original hyperspectral images,using the feature bands determined by MSGCF improved the recognition accuracy,and greatly shortened the running time.Moreover,the characteristic bands for quantifying the volume concentration of water-in-oil emulsions were determined,and a quantitative inversion model was constructed and applied to the AVIRIS image of the deepwater horizon oil spill event in 2010.This study verified the effectiveness of feature bands in identifying oil spill pollution types and quantifying concentration,laying foundation for rapid identification and quantification of marine oil spills and their emulsions on aircraft or in orbit. 展开更多
关键词 hyperspectral image spectral analysis dimensionality reduction multiscale superpixel level group clustering framework(MSGCF) double-branch dual-attention(DBDA)
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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification
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作者 Tsu-Yang Wu Haonan Li +1 位作者 Saru Kumari Chien-Ming Chen 《Computers, Materials & Continua》 SCIE EI 2024年第4期19-46,共28页
Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convol... Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification. 展开更多
关键词 Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification
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Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems
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作者 Jingcheng Zhang Xingjian Zhou +3 位作者 Dong Shen Qimeng Yu Lin Yuan Yingying Dong 《Phyton-International Journal of Experimental Botany》 SCIE 2024年第4期745-762,共18页
As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv.oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as ... As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv.oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was formed.The capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple sensors.Meanwhile, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale. 展开更多
关键词 Rice bacterial leaf blight analysis of spectral response multispectral data simulation vegetation indices cross-sensor disease monitoring
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Detection of Burned Areas through Spectral Indices Analysis of Sentinel-2A Satellite Images in the Abokouamékro Wildlife Reserve (Central, Côte D’Ivoire)
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作者 Bob Kouakou Kouadio Sié Ouattara +3 位作者 Alain Clément Jean-Marc Gala Bi Zaouri Jean-Luc Kouadio Kouassi Jean-Luc Edouard Kouakou N’guessan 《Open Journal of Applied Sciences》 2024年第1期205-222,共18页
In Côte d’Ivoire, the recurring and unregulated use of bushfires, which cause ecological damage, presents a pressing concern for the custodians of protected areas. This study aims to enhance our comprehension of... In Côte d’Ivoire, the recurring and unregulated use of bushfires, which cause ecological damage, presents a pressing concern for the custodians of protected areas. This study aims to enhance our comprehension of the dynamics of burnt areas within the Abokouamékro Wildlife Reserve (AWR) by employing the analysis of spectral indices derived from satellite imagery. The research methodology began with the calculation of mean indices and their corresponding spectral sub-indices, including NDVI, SAVI, NDWI, NDMI, BAI, NBR, TCW, TCG, and TCB, utilizing data from the Sentinel-2A satellite image dated January 17, 2022. Subsequently, a fuzzy classification model was applied to these various indices and sub-indices, guided by the degree of membership α, with the goal of effectively distinguishing between burned and unburned areas. Following the classification, the accuracies of the classified indices and sub-indices were validated using the coordinates of 100 data points collected within the AWR through GPS technology. The results revealed that the overall accuracy of all indices and sub-indices declines as the degree of membership α decreases from 1 to 0. Among the mean spectral indices, NDVI-mean, SAVI-mean, NDMI-mean exhibited the highest overall accuracies, achieving 97%, 95%, and 90%, respectively. These results closely mirrored those obtained by sub-indices using band 8 (NDVI-B8, SAVI-B8, and NDMI-B8), which yield respective overall accuracies of 93%, 92%, and 89%. At a degree of membership α = 1, the estimated burned areas for the most effective indices encompassed 2144.38 hectares for NDVI-mean, 1932.14 hectares for mean SAVI-mean, and 4947.13 hectares for mean NDMI-mean. A prospective approach involving the amalgamation of these three indices could have the potential to yield improved outcomes. This study could be a substantial contribution to the discrimination of bushfires in Côte d’Ivoire. 展开更多
关键词 spectral Indices WILDFIRE Burned Areas Abokouamékro Wildlife Reserve Côte D’Ivoire
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An Adaptive Spectral Conjugate Gradient Method with Restart Strategy
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作者 Zhou Jincheng Jiang Meixuan +2 位作者 Zhong Zining Wu Yanqiang Shao Hu 《数学理论与应用》 2024年第3期106-118,共13页
As a generalization of the two-term conjugate gradient method(CGM),the spectral CGM is one of the effective methods for solving unconstrained optimization.In this paper,we enhance the JJSL conjugate parameter,initiall... As a generalization of the two-term conjugate gradient method(CGM),the spectral CGM is one of the effective methods for solving unconstrained optimization.In this paper,we enhance the JJSL conjugate parameter,initially proposed by Jiang et al.(Computational and Applied Mathematics,2021,40:174),through the utilization of a convex combination technique.And this improvement allows for an adaptive search direction by integrating a newly constructed spectral gradient-type restart strategy.Then,we develop a new spectral CGM by employing an inexact line search to determine the step size.With the application of the weak Wolfe line search,we establish the sufficient descent property of the proposed search direction.Moreover,under general assumptions,including the employment of the strong Wolfe line search for step size calculation,we demonstrate the global convergence of our new algorithm.Finally,the given unconstrained optimization test results show that the new algorithm is effective. 展开更多
关键词 Unconstrained optimization spectral conjugate gradient method Restart strategy Inexact line search Global convergence
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An Eight Component Integrable Hamiltonian Hierarchy from a Reduced Seventh-Order Matrix Spectral Problem
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作者 Savitha Muthanna Wen-Xiu Ma 《Journal of Applied Mathematics and Physics》 2024年第6期2102-2111,共10页
We present an eight component integrable Hamiltonian hierarchy, based on a reduced seventh order matrix spectral problem, with the aim of aiding the study and classification of multicomponent integrable models and the... We present an eight component integrable Hamiltonian hierarchy, based on a reduced seventh order matrix spectral problem, with the aim of aiding the study and classification of multicomponent integrable models and their underlying mathematical structures. The zero-curvature formulation is the tool to construct a recursion operator from the spatial matrix problem. The second and third set of integrable equations present integrable nonlinear Schrödinger and modified Korteweg-de Vries type equations, respectively. The trace identity is used to construct Hamiltonian structures, and the first three Hamiltonian functionals so generated are computed. 展开更多
关键词 Matrix spectral Problem Zero Curvature Equation Lax Pair Integrable Hierarchy NLS Equations mKdV Equations Hamiltonian Structure Lie Bracke
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Sanxingdui Cultural Relics Recognition Algorithm Based on Hyperspectral Multi-Network Fusion 被引量:2
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作者 Shi Qiu Pengchang Zhang +3 位作者 Xingjia Tang Zimu Zeng Miao Zhang Bingliang Hu 《Computers, Materials & Continua》 SCIE EI 2023年第12期3783-3800,共18页
Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history,science,culture,art and research.However,mainstream analytical methods are contacting and detrimental,which is unfa... Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history,science,culture,art and research.However,mainstream analytical methods are contacting and detrimental,which is unfavorable to the protection of cultural relics.This paper improves the accuracy of the extraction,location,and analysis of artifacts using hyperspectral methods.To improve the accuracy of cultural relic mining,positioning,and analysis,the segmentation algorithm of Sanxingdui cultural relics based on the spatial spectrum integrated network is proposed with the support of hyperspectral techniques.Firstly,region stitching algorithm based on the relative position of hyper spectrally collected data is proposed to improve stitching efficiency.Secondly,given the prominence of traditional HRNet(High-Resolution Net)models in high-resolution data processing,the spatial attention mechanism is put forward to obtain spatial dimension information.Thirdly,in view of the prominence of 3D networks in spectral information acquisition,the pyramid 3D residual network model is proposed to obtain internal spectral dimensional information.Fourthly,four kinds of fusion methods at the level of data and decision are presented to achieve cultural relic labeling.As shown by the experiment results,the proposed network adopts an integrated method of data-level and decision-level,which achieves the optimal average accuracy of identification 0.84,realizes shallow coverage of cultural relics labeling,and effectively supports the mining and protection of cultural relics. 展开更多
关键词 SANXINGDUI cultural relic spatial features spectral features HYPERspectral INTEGRATION
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