Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar dynamics.This paper introduces an innovative unsupervised deep learning optical flow model designed to calcu...Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar dynamics.This paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar images.The proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow labels.Additionally,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow outliers.We apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar Telescope.Comparative experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index Measure.Moreover,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity forecasting.The code is available at https://github.com/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.展开更多
Measurement of vegetation coverage on a small scale is the foundation for the monitoring of changes in vegetation coverage and of the inversion model of monitoring vegetation coverage on a large scale by remote sensin...Measurement of vegetation coverage on a small scale is the foundation for the monitoring of changes in vegetation coverage and of the inversion model of monitoring vegetation coverage on a large scale by remote sensing. Using the object-oriented analytical software, Definiens Professional 5, a new method for calculating vegetation coverage based on high-resolution images （aerial photographs or near-surface photography） is proposed. Our research supplies references to remote sensing measurements of vegetation coverage on a small scale and accurate fundamental data for the inversion model of vegetation coverage on a large and intermediate scale to improve the accuracy of remote sensing monitoring of changes in vegetation coverage.展开更多
Nowadays, remote sensing imagery, especially with its high spatialresolution, has become an indispensable tool to provide timely up-gradation of urban land use andland cover information, which is a prerequisite for pr...Nowadays, remote sensing imagery, especially with its high spatialresolution, has become an indispensable tool to provide timely up-gradation of urban land use andland cover information, which is a prerequisite for proper urban planning and management. Thepossible method described in the present paper to obtain urban land use types is based on theprinciple that land use can be derived from the land cover existing in a neighborhood. Here, movingwindow is used to represent the spatial pattern of land cover within a neighborhood and seven windowsizes (61mx61m, 68mx68m, 75mx75m, 87mx87m, 99mx99m, 110mx110m and 121mxl21m) are applied todetermining the most proper window size. Then, the unsupervised method of ISODATA is employed toclassify the layered land cover density maps obtained by the moving window. The results of accuracyevaluation show that the window size of 99mx99m is proper to infer urban land use categories and theproposed method has produced a land use map with a total accuracy of 85%.展开更多
Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to pro...Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations.This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC problem.The new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential areas.First,it identifies tree canopy regions using a deep learning network from high-resolution imagery.It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing.Finally,it calculates and describes the number of individual trees and tree canopies.The proposed approach is experimented with the data from Shanghai,China.Our results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland scene.Meanwhile,the R^(2) value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,respectively.These results confirm that the proposed method is robust enough for urban tree planning and management.展开更多
Ephemeral gullies,which are widely developed worldwide and threaten farmlands,have aroused a growing concern.Identifying and mapping gullies are generally considered prerequisites of gully erosion assessment.However,e...Ephemeral gullies,which are widely developed worldwide and threaten farmlands,have aroused a growing concern.Identifying and mapping gullies are generally considered prerequisites of gully erosion assessment.However,ephemeral gully mapping remains a challenge.In this study,we proposed a flow-directional detection for identifying ephemeral gullies from high-resolution images and digital elevation models(DEMs).Ephemeral gullies exhibit clear linear features in high-resolution images.An edge detection operator was initially used to identify linear features from high-resolution images.Then,according to gully erosion mechanism,the flow-directional detection was designed.Edge images obtained from edge detection and flow directions obtained from DEMs were used to implement the flow-directional detection that detects ephemeral gullies along the flow direction.Results from ten study areas in the Loess Plateau of China showed that ranges of precision,recall,and Fmeasure are 6 o.66%-90.47%,65.74%-94.98%,and63.10%-91.93%,respectively.The proposed method is flexible and can be used with various images and DEMs.However,analysis of the effect of DEM resolution and accuracy showed that DEM resolution only demonstrates a minor effect on the detection results.Conversely,DEM accuracy influences the detection result and is more important than the DEM resolution.The worse the vertical accuracy of DEM,the lower the performance of the flow-directional detection will be.This work is beneficial to research related to monitoring gully erosion and assessing soil loss.展开更多
On the basis of a thorough understanding of the physical characteristics of remote sensing image, this paper employs the theories of wavelet transform and signal sampling to develop a new image fusion algorithm. The a...On the basis of a thorough understanding of the physical characteristics of remote sensing image, this paper employs the theories of wavelet transform and signal sampling to develop a new image fusion algorithm. The algorithm has been successfully applied to the image fusion of SPOT PAN and TM of Guangdong province, China. The experimental results show that a perfect image fusion can be built up by using the image analytical solution and re-construction in the image frequency domain based on the physical characteristics of the image formation. The method has demonstrated that the results of the image fusion do not change spectral characteristics of the original image.展开更多
Small-object detection has long been a challenge.High-megapixel cameras are used to solve this problem in industries.However,current detectors are inefficient for high-resolution images.In this work,we propose a new m...Small-object detection has long been a challenge.High-megapixel cameras are used to solve this problem in industries.However,current detectors are inefficient for high-resolution images.In this work,we propose a new module called Pre-Locate Net,which is a plug-and-play structure that can be combined with most popular detectors.We inspire the use of classification ideas to obtain candidate regions in images,greatly reducing the amount of calculation,and thus achieving rapid detection in high-resolution images.Pre-Locate Net mainly includes two parts,candidate region classification and behavior classification.Candidate region classification is used to obtain a candidate region,and behavior classification is used to estimate the scale of an object.Different follow-up processing is adopted according to different scales to balance the variance of the network input.Different from the popular candidate region generation method,we abandon the idea of regression of a bounding box and adopt the concept of classification,so as to realize the prediction of a candidate region in the shallow network.We build a high-resolution dataset of aircraft and landing gears covering complex scenes to verify the effectiveness of our method.Compared to state-of-the-art detectors(e.g.,Guided Anchoring,Libra-RCNN,and FASF),our method achieves the best m AP of 94.5 on 1920×1080 images at 16.7 FPS.展开更多
A large number of debris flow disasters(called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly ...A large number of debris flow disasters(called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning(TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network(CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used imag...While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.展开更多
The commercial high-resolution imaging satellite with 1 m spatial resolution IKONOS is an important data source of information for urban planning and geographical information system (GIS) applications. In this paper, ...The commercial high-resolution imaging satellite with 1 m spatial resolution IKONOS is an important data source of information for urban planning and geographical information system (GIS) applications. In this paper, a morphological method is proposed. The proposed method combines the automatic thresholding and morphological operation techniques to extract the road centerline of the urban environment. This method intends to solve urban road centerline problems, vehicle, vegetation, building etc. Based on this morphological method, an object extractor is designed to extract road networks from highly remote sensing images. Some filters are applied in this experiment such as line reconstruction and region filling techniques to connect the disconnected road segments and remove the small redundant. Finally, the thinning algorithm is used to extract the road centerline. Experiments have been conducted on a high-resolution IKONOS and QuickBird images showing the efficiency of the proposed method.展开更多
Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,w...Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,we propose HUANNet(High-Resolution Unified Attention Network),a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework,while optimizing computational distribution across parallel branches.HUANNet introduces three core modules:the High-Resolution Attention Module(HRAM),which enhances feature extraction by optimizing multiresolution feature fusion;the Unified Multi-Scale Attention Module(UMAM),which integrates spatial,channel,and convolutional kernel information through an attention mechanism applied across multiple levels of the network;and the Grid-Assisted Point Matching Module(GPMM),which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms.Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD,demonstrating the effectiveness and versatility of HUANNet.展开更多
Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-...Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.展开更多
Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image dis...Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes ...Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.展开更多
Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi...Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
The fractal characteristics of tidal creeks in the Gaizhou Beach are analyzed based on high-resolution images fusionof Landsat TM and ERS2, and then the graphic models and characteristics of converse information tree ...The fractal characteristics of tidal creeks in the Gaizhou Beach are analyzed based on high-resolution images fusionof Landsat TM and ERS2, and then the graphic models and characteristics of converse information tree of tidalcreeks in the Gaizhou Beach are established. A calculation model is established based on the above results, and at thesame time, quantitative calculation of the evolution characteristics and the diversity between the northern and thesouthern parts of the Gaizhou Beach is carried out. By the supervised classification of these images, distribution andareas of high tidal flats, middle tidal flats and low tidal flats in the Gaizhou Beach are studied quantitatively, and imagecharactistics of seashell habitats in the Gaizhou Beach and the correlation between mudflat distribution and seashellhabitats are studied. At last, the engineering problems in the Gaizhou Beach are discussed.展开更多
Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance.To quantify the mineral dissemination and pore space distribution of an ore particle,a...Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance.To quantify the mineral dissemination and pore space distribution of an ore particle,a cylindrical copper oxide ore sample(I center dot 4.6 mm x 5.6 mm)was scanned using high-resolution X-ray computed tomography(HRXCT),a nondestructive imaging technology,at a spatial resolution of 4.85 mu m.Combined with three-dimensional(3D)image analysis techniques,the main mineral phases and pore space were segmented and the volume fraction of each phase was calculated.In addition,the mass fraction of each mineral phase was estimated and the result was validated with that obtained using traditional techniques.Furthermore,the pore phase features,including the pore size distribution,pore surface area,pore fractal dimension,pore centerline,and the pore connectivity,were investigated quantitatively.The pore space analysis results indicate that the pore size distribution closely fits a log-normal distribution and that the pore space morphology is complicated,with a large surface area and low connectivity.This study demonstrates that the combination of HRXCT and 3D image analysis is an effective tool for acquiring 3D mineralogical and pore structural data.展开更多
基金supported by the National Natural Science Foundation of China(NSFC,Grant Nos.12063002 and 12163004).
文摘Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar dynamics.This paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar images.The proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow labels.Additionally,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow outliers.We apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar Telescope.Comparative experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index Measure.Moreover,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity forecasting.The code is available at https://github.com/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.
基金funded by the National Natural Science Foundation of China(Grant No.40571029).
文摘Measurement of vegetation coverage on a small scale is the foundation for the monitoring of changes in vegetation coverage and of the inversion model of monitoring vegetation coverage on a large scale by remote sensing. Using the object-oriented analytical software, Definiens Professional 5, a new method for calculating vegetation coverage based on high-resolution images （aerial photographs or near-surface photography） is proposed. Our research supplies references to remote sensing measurements of vegetation coverage on a small scale and accurate fundamental data for the inversion model of vegetation coverage on a large and intermediate scale to improve the accuracy of remote sensing monitoring of changes in vegetation coverage.
基金Under the auspices of Jiangsu Provincial Natural ScienceFoundation(No .BK2002420 )
文摘Nowadays, remote sensing imagery, especially with its high spatialresolution, has become an indispensable tool to provide timely up-gradation of urban land use andland cover information, which is a prerequisite for proper urban planning and management. Thepossible method described in the present paper to obtain urban land use types is based on theprinciple that land use can be derived from the land cover existing in a neighborhood. Here, movingwindow is used to represent the spatial pattern of land cover within a neighborhood and seven windowsizes (61mx61m, 68mx68m, 75mx75m, 87mx87m, 99mx99m, 110mx110m and 121mxl21m) are applied todetermining the most proper window size. Then, the unsupervised method of ISODATA is employed toclassify the layered land cover density maps obtained by the moving window. The results of accuracyevaluation show that the window size of 99mx99m is proper to infer urban land use categories and theproposed method has produced a land use map with a total accuracy of 85%.
基金supported by the project funded by International Research Center of Big Data for Sustainable 740 Development Goals[Grant Number CBAS2022GSP07]Fundamental Research Funds for the Central Universities,Chongqing Natural Science Foundation[Grant Number CSTB2022NSCQMSX 2069]Ministry of Education of China[Grant Number 19JZD023].
文摘Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations.This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC problem.The new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential areas.First,it identifies tree canopy regions using a deep learning network from high-resolution imagery.It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing.Finally,it calculates and describes the number of individual trees and tree canopies.The proposed approach is experimented with the data from Shanghai,China.Our results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland scene.Meanwhile,the R^(2) value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,respectively.These results confirm that the proposed method is robust enough for urban tree planning and management.
基金funded by the National Natural Science Foundation of China (Grant No. 41930102, 41971333, 41771415, and 41701449)the Priority Academic Program Development of Jiangsu Higher Education Institutions (Grant No. 164320H116)the Open Fund of Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution (Grant No. KLSPWSEPA04)。
文摘Ephemeral gullies,which are widely developed worldwide and threaten farmlands,have aroused a growing concern.Identifying and mapping gullies are generally considered prerequisites of gully erosion assessment.However,ephemeral gully mapping remains a challenge.In this study,we proposed a flow-directional detection for identifying ephemeral gullies from high-resolution images and digital elevation models(DEMs).Ephemeral gullies exhibit clear linear features in high-resolution images.An edge detection operator was initially used to identify linear features from high-resolution images.Then,according to gully erosion mechanism,the flow-directional detection was designed.Edge images obtained from edge detection and flow directions obtained from DEMs were used to implement the flow-directional detection that detects ephemeral gullies along the flow direction.Results from ten study areas in the Loess Plateau of China showed that ranges of precision,recall,and Fmeasure are 6 o.66%-90.47%,65.74%-94.98%,and63.10%-91.93%,respectively.The proposed method is flexible and can be used with various images and DEMs.However,analysis of the effect of DEM resolution and accuracy showed that DEM resolution only demonstrates a minor effect on the detection results.Conversely,DEM accuracy influences the detection result and is more important than the DEM resolution.The worse the vertical accuracy of DEM,the lower the performance of the flow-directional detection will be.This work is beneficial to research related to monitoring gully erosion and assessing soil loss.
基金ProjectsupportedbytheNationalNaturalScienceFoundationofChina (No .40 0 2 30 0 4 ) .
文摘On the basis of a thorough understanding of the physical characteristics of remote sensing image, this paper employs the theories of wavelet transform and signal sampling to develop a new image fusion algorithm. The algorithm has been successfully applied to the image fusion of SPOT PAN and TM of Guangdong province, China. The experimental results show that a perfect image fusion can be built up by using the image analytical solution and re-construction in the image frequency domain based on the physical characteristics of the image formation. The method has demonstrated that the results of the image fusion do not change spectral characteristics of the original image.
基金the National Science Fund for Distinguished Young Scholars of China (No. 51625501)the Aeronautical Science Foundation of China (No. 201946051002)
文摘Small-object detection has long been a challenge.High-megapixel cameras are used to solve this problem in industries.However,current detectors are inefficient for high-resolution images.In this work,we propose a new module called Pre-Locate Net,which is a plug-and-play structure that can be combined with most popular detectors.We inspire the use of classification ideas to obtain candidate regions in images,greatly reducing the amount of calculation,and thus achieving rapid detection in high-resolution images.Pre-Locate Net mainly includes two parts,candidate region classification and behavior classification.Candidate region classification is used to obtain a candidate region,and behavior classification is used to estimate the scale of an object.Different follow-up processing is adopted according to different scales to balance the variance of the network input.Different from the popular candidate region generation method,we abandon the idea of regression of a bounding box and adopt the concept of classification,so as to realize the prediction of a candidate region in the shallow network.We build a high-resolution dataset of aircraft and landing gears covering complex scenes to verify the effectiveness of our method.Compared to state-of-the-art detectors(e.g.,Guided Anchoring,Libra-RCNN,and FASF),our method achieves the best m AP of 94.5 on 1920×1080 images at 16.7 FPS.
基金supported by the National Natural Science Foundation of China(41701499)the Sichuan Science and Technology Program(2018GZ0265)the Geomatics Technology and Application Key Laboratory of Qinghai Province(QHDX-2018-07)
文摘A large number of debris flow disasters(called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning(TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network(CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
文摘While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.
文摘The commercial high-resolution imaging satellite with 1 m spatial resolution IKONOS is an important data source of information for urban planning and geographical information system (GIS) applications. In this paper, a morphological method is proposed. The proposed method combines the automatic thresholding and morphological operation techniques to extract the road centerline of the urban environment. This method intends to solve urban road centerline problems, vehicle, vegetation, building etc. Based on this morphological method, an object extractor is designed to extract road networks from highly remote sensing images. Some filters are applied in this experiment such as line reconstruction and region filling techniques to connect the disconnected road segments and remove the small redundant. Finally, the thinning algorithm is used to extract the road centerline. Experiments have been conducted on a high-resolution IKONOS and QuickBird images showing the efficiency of the proposed method.
基金funded by the National Natural Science Foundation of China(62273213,62472262,62572287)Natural Science Foundation of Shandong Province(ZR2024MF144)+1 种基金Natural Science Foundation of Shandong Province for Innovation and Development Joint Funds(ZR2022LZH001)Taishan Scholarship Construction Engineering.
文摘Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,we propose HUANNet(High-Resolution Unified Attention Network),a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework,while optimizing computational distribution across parallel branches.HUANNet introduces three core modules:the High-Resolution Attention Module(HRAM),which enhances feature extraction by optimizing multiresolution feature fusion;the Unified Multi-Scale Attention Module(UMAM),which integrates spatial,channel,and convolutional kernel information through an attention mechanism applied across multiple levels of the network;and the Grid-Assisted Point Matching Module(GPMM),which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms.Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD,demonstrating the effectiveness and versatility of HUANNet.
基金supported by the Natural Science Foundation of Hubei Provincial Department of Education(D20232101)Shandong Second Medical University 2024 Affiliated Hospital(Teaching Hospital)Scientific Research Development Fund Project(2024FYQ026)+3 种基金the innovative Research Programme of Xiangyang No.1 People’s Hospital(XYY2023ZY01)Faculty Development Grants of Xiangyang No.1 People’s Hospital Affiliated to Hubei University of Medicine(XYY2023D05)Joint supported by Hubei Provincial Natural Science Foundation and Xiangyang of China(2025AFD091)Traditional Chinese Medicine Scientific Research Project of Hubei Provincial Administration of Traditional Chinese Medicine(ZY2025D019).
文摘Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.SJCX24_1332)Jiangsu Province Education Science Planning Project in 2024(Grant No.B-b/2024/01/122)High-Level Talent Scientific Research Foundation of Jinling Institute of Technology,China(Grant No.jit-b-201918).
文摘Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金supported by the National Natural Science Foundation of China(62376106)The Science and Technology Development Plan of Jilin Province(20250102212JC).
文摘Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.
基金funded by University of Transport and Communications(UTC)under grant number T2025-CN-004.
文摘Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
基金This study was supported by the Project of“863”Marine Monitor of Hi-Tech Research and Development Program of China under contract No.2003AA604040.
文摘The fractal characteristics of tidal creeks in the Gaizhou Beach are analyzed based on high-resolution images fusionof Landsat TM and ERS2, and then the graphic models and characteristics of converse information tree of tidalcreeks in the Gaizhou Beach are established. A calculation model is established based on the above results, and at thesame time, quantitative calculation of the evolution characteristics and the diversity between the northern and thesouthern parts of the Gaizhou Beach is carried out. By the supervised classification of these images, distribution andareas of high tidal flats, middle tidal flats and low tidal flats in the Gaizhou Beach are studied quantitatively, and imagecharactistics of seashell habitats in the Gaizhou Beach and the correlation between mudflat distribution and seashellhabitats are studied. At last, the engineering problems in the Gaizhou Beach are discussed.
基金financially supported by the National Natural Science Foundation of China(No.51304076)the Natural Science Foundation of Hunan Province,China(No.14JJ4064)
文摘Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance.To quantify the mineral dissemination and pore space distribution of an ore particle,a cylindrical copper oxide ore sample(I center dot 4.6 mm x 5.6 mm)was scanned using high-resolution X-ray computed tomography(HRXCT),a nondestructive imaging technology,at a spatial resolution of 4.85 mu m.Combined with three-dimensional(3D)image analysis techniques,the main mineral phases and pore space were segmented and the volume fraction of each phase was calculated.In addition,the mass fraction of each mineral phase was estimated and the result was validated with that obtained using traditional techniques.Furthermore,the pore phase features,including the pore size distribution,pore surface area,pore fractal dimension,pore centerline,and the pore connectivity,were investigated quantitatively.The pore space analysis results indicate that the pore size distribution closely fits a log-normal distribution and that the pore space morphology is complicated,with a large surface area and low connectivity.This study demonstrates that the combination of HRXCT and 3D image analysis is an effective tool for acquiring 3D mineralogical and pore structural data.
