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Object-based classification of hyperspectral data using Random Forest algorithm 认领 引用 被引量：8
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作者 Saeid Amini Saeid Homayouni +1 位作者 Abdolreza Safari Ali A.Darvishsefat 《Geo-Spatial Information Science》 SCIE EI CSCD 2018年第2期127-138,共12页
This paper presents a new framework for object-based classification of high-resolution hyperspectral data.This multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)algori... This paper presents a new framework for object-based classification of high-resolution hyperspectral data.This multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)algorithms.The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images.Given the high number of input features,an automatic method is needed for estimation of this parameter.Moreover,we used the Variable Importance(VI),one of the outputs of the RFC,to determine the importance of each image band.Then,based on this parameter and other required parameters,the image is segmented into some homogenous regions.Finally,the RFC is carried out based on the characteristics of segments for converting them into meaningful objects.The proposed method,as well as,the conventional pixel-based RFC and Support Vector Machine(SVM)method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics.These data were acquired by the HyMap,the Airborne Prism Experiment(APEX),and the Compact Airborne Spectrographic Imager(CASI)hyperspectral sensors.The experimental results show that the proposed method is more consistent for land cover mapping in various areas.The overall classification accuracy(OA),obtained by the proposed method was 95.48,86.57,and 84.29%for the HyMap,the APEX,and the CASI datasets,respectively.Moreover,this method showed better efficiency in comparison to the spectralbased classifications because the OAs of the proposed method was 5.67 and 3.75%higher than the conventional RFC and SVM classifiers,respectively. 展开更多
关键词 Object-based classification Random Forest algorithm multi-resolution segmentation(MRS) hyperspectral imagery
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Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability assessment of marine disaster 认领 引用 被引量：2
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作者 YANG Fengshuo YANG Xiaomei +3 位作者 WANG Zhihua LU Chen LI Zhi LIU Yueming 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2019年第6期1955-1970,共16页
Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free a... Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas. 展开更多
关键词 coastal area marine disaster vulnerability assessment remote sensing land use/cover object-based image analysis(OBIA)
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Object-based Classification of Baltic Sea Ice Extent and Concentration in Winter 2011 认领 引用 被引量：2
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作者 Aleksandra Mazur Adam Krezel 《Journal of Earth Science and Engineering》 2012年第8期488-495,共8页
The Baltic Sea is a brackish, mediterranean sea located in the middle latitudes of Europe. It is seasonally covered with ice. The ice covered areas during a typical winter are the Bothnian Bay, the Gulf of Finnland an... The Baltic Sea is a brackish, mediterranean sea located in the middle latitudes of Europe. It is seasonally covered with ice. The ice covered areas during a typical winter are the Bothnian Bay, the Gulf of Finnland and the Gulf of Riga. Sea ice plays an important role in dynamic and thermodynamic processes and also has a strong impact on the heat budget of the sea. Also a large part of transport goes by sea, and there is a need to create ice charts to make the marine transport safe. Because of high cloudiness in winter season and small amount of light in the northern part of the Baltic Sea, radar data are the most important remote sensing source of sea ice information. The main goal of the following studies is classification of the Baltic sea ice cover using radar data. The ENVISAT ASAR （Advanced Synthetic Aperture Radar） acquires data in five different modes. In the following studies ASAR Wide Swath Mode data were used. The Wide Swath Mode, using the ScanSAR technique provides medium resolution images （150 m） over a swath of 405 kin, at HH or VV polarization. In following work data from February 13th, February 24th and April 6th, 2011, representing three different sea ice situations were chosen. OBIA （object-based image analysis） methods and texture parameters were used to create sea ice extent and sea ice concentration charts. Based on object-based methods, it can separate single sea ice floes within the ice pack and calculate more accurately sea ice concentration. 展开更多
关键词 Baltic Sea sea ice ENVISAT ASAR object-based image analysis.
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Object-Based Classification of Urban Distinct Sub-Elements Using High Spatial Resolution Orthoimages and DSM Layers 认领 引用
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作者 Ali Nouh Mabdeh A'kif Al-Fugara Mu’men Al jarah 《Journal of Geographic Information System》 2018年第4期323-343,共21页
This paper aims to assess the ways in which multi-resolution object-based classification methods can be used to group urban environments made up of a mixture of buildings, sub-elements such as car parks, roads, shades... This paper aims to assess the ways in which multi-resolution object-based classification methods can be used to group urban environments made up of a mixture of buildings, sub-elements such as car parks, roads, shades and pavements and foliage such as grass and trees. This involves using both unmanned aerial vehicles (UAVs) which provide high-resolution mosaic Orthoimages and generate a Digital Surface Model (DSM). For the study area chosen for this paper, 400 Orthoimages with a spatial resolution of 7 cm each were used to build the Orthoimages and DSM, which were georeferenced using well distributed network of ground control points (GCPs) of 12 reference points (RMSE = 8 cm). As these were combined with onboard RTK-GNSS-enabled 2-frequency receivers, they were able to provide absolute block orientation which had a similar accuracy range if the data had been collected by traditional indirect sensor orientation. Traditional indirect sensor orientation involves the GNSS receiver in the UAV receiving a differential signal from the base station through a communication link. This allows for the precise position of the UAV to be established, as the RTK uses correction, allowing position, velocity, altitude and heading to tracked, as well as the measurement of raw sensor data. By assessing the results of the confusion matrices, it can be seen that the overall accuracy of the object-oriented classification was 84.37%. This has an overall Kappa of 0.74 and the data that had poor classification accuracy included shade, parking lots and concrete pavements. These had a producer accuracy (precision) of 81%, 74% and 74% respectively, while lakes and solar panels each scored 100% in comparison, meaning that they had good classification accuracy. 展开更多
关键词 Object-Oriented Classification Real Time Kinematics DSM UAV Orthoimages Mosaic Urban Distinct Sub-Elements
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Object-based classification approach for greenhouse mapping using Landsat-8 imagery 认领 引用 被引量：19
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作者 Wu Chaofan Deng Jinsong +2 位作者 Wang Ke Ma Ligang Amir Reza Shah Tahmassebi 《International Journal of Agricultural and Biological Engineering》 SCIE 2016年第1期79-88,I0005,共10页
Suburban greenhouses with intensive agricultural productivity have increasingly influenced the daily diet and vegetable supply in Chinese cities.With their enormous input of fertilizers and pesticides,greenhouses have... Suburban greenhouses with intensive agricultural productivity have increasingly influenced the daily diet and vegetable supply in Chinese cities.With their enormous input of fertilizers and pesticides,greenhouses have considerably changed the local soil quality and environmental risk factors.The ability to obtain timely and accurate information regarding the spatial distribution of greenhouses could make an important contribution to local agricultural management and soil protection.This paper attempts to present a practical framework for extracting suburban greenhouses,integrating remote sensing data from Landsat-8 and object-oriented classification.Inheritance classification was implemented,and various properties,including texture and neighborhood features in addition to spectral information,were investigated through the popular random forest technique for feature selection prior to SVM classification to improve the mapping accuracy.The results demonstrated that object-based classification incorporating non-spectral features yielded a significant improvement compared with the classification results obtained using only the spectral information in traditional per-pixel classification.Both the producer’s and user’s accuracy were higher than 85%for greenhouse identification.Although it remained a challenge to completely distinguish greenhouses from sparse plants,the final greenhouse map indicated that the proposed object-based classification scheme,providing multiple feature selections and multi-scale analysis,yielded worthwhile information when applied to a continuous series of the freely available Landsat-8 imagery data. 展开更多
关键词 greenhouse mapping Landsat-8 object-based classification feature selection multi-scale
Identifying Alpine Wetlands in the Damqu River Basin in the Source Area of the Yangtze River Using Object-based Classification Method 认领 引用 被引量：2
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作者 张继平 张镱锂 +2 位作者 刘林山 丁明军 张学儒 《Journal of Resources and Ecology》 CSCD 2011年第2期186-192,共7页
Alpine wetlands are very sensitive to global change, have great impacts on the hydrological condition of rivers, and are closely related to peoples＇ living in lower reaches. It is essential to monitor alpine wetland ... Alpine wetlands are very sensitive to global change, have great impacts on the hydrological condition of rivers, and are closely related to peoples＇ living in lower reaches. It is essential to monitor alpine wetland changes to appropriately manage and protect wetland resources; however, it is quite difficult to accurately extract such information from remote sensing images due to spectral confusion and arduous field verification. In this study, we identified different wetland types in the Damqu River Basin located in the Yangze River source region from Landsat remote sensing data using the object-based method. In order to ensure the interpretation accuracy of wetland, a digital elevation model （DEM） and its derived data （slope, aspect）, Normalized Difference Vegetation Index （NDVI）, Normalized Difference Water Index （NDWI）, and Kauth-Thomas transformation were considered as the components of the spectral characteristics of wetland types. The spectral characteristics, texture features and spatial structure characteristics of each wetland type were comprehensively analyzed based on the success of image segmentation. The extraction rules for each wetland type were established by determining the thresholds of the spatial, texture and spectral attributes of typical parameter layers according to their histogram statistics. The classification accuracy was assessed using error matrixes and field survey verification data. According to the accuracy assessment, the total accuracy of image classification was 89%. 展开更多
关键词 alpine wetland remote sensing object-based classification Damqu River Basin
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Multimodal Signal Processing of ECG Signals with Time-Frequency Representations for Arrhythmia Classification 认领 引用 被引量：1
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作者 Yu Zhou Jiawei Tian Kyungtae Kang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期990-1017,共28页
Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin... Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification. 展开更多
关键词 Electrocardiogram arrhythmia classification multimodal time-frequency representation
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Review of the classification and related terminology of fine-grained sedimentary rocks 认领 引用 被引量：1
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作者 ZHU Rukai SUN Longde +2 位作者 ZOU Caineng CHEN Yang MIAO Xue 《Petroleum Exploration and Development》 SCIE 2026年第1期61-78,共18页
Through tracing the background and customary usage of classification of fine-grained sedimentary rocks and terminology,and comparing current“sedimentary petrology”textbooks and monographs,this paper proposes a class... Through tracing the background and customary usage of classification of fine-grained sedimentary rocks and terminology,and comparing current“sedimentary petrology”textbooks and monographs,this paper proposes a classification scheme for fine-grained sedimentary rocks and clarifies related terminology.The comprehensive analysis indicates that the classification of clastic rocks,volcanic clastic rocks,chemical rocks,and biogenic(carbonate)rocks is unified,and the definitions of terms such as lamination,bedding and beds are consistent.However,there is a disagreement on the definition of“mud”.European and American scholars commonly use the term“mud”to include silt and clay(particle size less than 0.0625 mm).Chinese scholars equate the term“mud”to“clay”(particle size less than 0.0039 mm or less than 0.01 mm).Combined with the discussion on terms such as sedimentary structures(bedding,lamination and lamellation),shale,mudstone,mudrocks/argillaceous rocks and mud shale,it is recommended to use“fine-grained sedimentary rocks”as the general term for all sedimentary rocks composed of fine-grained materials with particle size less than 0.0625 mm,including claystone/mudrocks and siltstone.Claystone/mudrocks are further classified into argillaceous(or clayey)mudstone/shale,calcareous mudstone/shale,siliceous mudstone/shale,silty mudstone/shale and silt-containing mudstone/shale.Argillaceous(or clayey)mudstone/shale emphasizes a content of clay minerals or clay-sized particles exceeding 50%.Other mudstones/shales emphasize a content of particles(particle size less than 0.0625 mm)exceeding 50%.The commonly referred term“shale”should not include siltstone.It is necessary to establish a reasonable,standardized,and applicable classification scheme for fine-grained sedimentary rocks in the future.An integrated shale microfacies research at the thin-section scale should be carried out,and combined with well logging data interpretation and seismic attribute analysis,a geological model of lithology/lithofacies will be iteratively upgraded to accurately determine sweet layer,locate target layer,and evaluate favorable area. 展开更多
关键词 fine-grained sedimentary rock shale mudstone clay shale oil shale gas lamellation shale microfacies classification scheme fine-grained sedimentology
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Application of a novel adverse event classification scale in a Latin American gastrointestinal endoscopy unit 认领 引用 被引量：1
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作者 Oscar Corsi Richard Martinez +7 位作者 Joaquín Aguirre Isidora Friedrich Victoria Galeno Vicente Jimenez Pamela Briones Luis Antonio Díaz Alberto Espino Jose Ignacio Vargas 《World Journal of Gastrointestinal Endoscopy》 2026年第1期87-94,共8页
BACKGROUND Accurate classification of adverse events(AEs)in gastrointestinal endoscopy is essential for safety monitoring and quality improvement.The American Society for Gastrointestinal Endoscopy(ASGE)lexicon is wid... BACKGROUND Accurate classification of adverse events(AEs)in gastrointestinal endoscopy is essential for safety monitoring and quality improvement.The American Society for Gastrointestinal Endoscopy(ASGE)lexicon is widely used,while the classification for AEs in gastrointestinal endoscopy(AGREE)is a recently proposed alternative aiming for broader applicability.AIM To compare the agreement and correlation between the AGREE and ASGE classification systems using real-world data from a Latin American academic endoscopy unit.METHODS A retrospective analysis of a prospective registry was conducted at a tertiary center in Chile,encompassing all endoscopy-related AEs from 2009 to 2022.Each AE was independently graded using both ASGE and AGREE classification systems by two blinded reviewers per system.Interobserver agreement was calculated using Cohen’s Kappa,and inter-scale correlation was assessed using Spearman’s rank test.RESULTS Of 176655 procedures performed,235 AEs(0.13%)were included.Most events were related to therapeutic procedures,and the most common AEs were cardiorespiratory(42.1%),bleeding(20.9%),and perforation(17.0%).The ASGE system identified 42.1%of cases as incidents and 57.9%as AEs(Kappa=0.83).AGREE classified 46.0%as non-AEs and 54.0%as AEs(Kappa=0.74).A strong correlation between both systems was observed(ρ=0.89;P<0.001).CONCLUSION The AGREE classification strongly correlates with the ASGE lexicon but excludes more cases as non-AEs and shows slightly lower interobserver agreement.These findings support AGREE as a feasible alternative for AE grading in gastrointestinal endoscopy,particularly in diverse clinical environments. 展开更多
关键词 Gastrointestinal endoscopy Adverse events classification Adverse events in gastrointestinal endoscopy American Society for Gastrointestinal Endoscopy Patient safety Latin America
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CANNSkin:A Convolutional Autoencoder Neural Network-Based Model for Skin Cancer Classification 认领 引用
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作者 Abdul Jabbar Siddiqui Saheed Ademola Bello +3 位作者 Muhammad Liman Gambo Abdul Khader Jilani Saudagar Mohamad A.Alawad Amir Hussain 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1142-1165,共24页
Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities,variations in skin texture,the presence of hair,and inconsistent illumination.Deep learning models have shown promise in assisting ... Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities,variations in skin texture,the presence of hair,and inconsistent illumination.Deep learning models have shown promise in assisting early detection,yet their performance is often limited by the severe class imbalance present in dermoscopic datasets.This paper proposes CANNSkin,a skin cancer classification framework that integrates a convolutional autoencoder with latent-space oversampling to address this imbalance.The autoencoder is trained to reconstruct lesion images,and its latent embeddings are used as features for classification.To enhance minority-class representation,the Synthetic Minority Oversampling Technique(SMOTE)is applied directly to the latent vectors before classifier training.The encoder and classifier are first trained independently and later fine-tuned end-to-end.On the HAM10000 dataset,CANNSkin achieves an accuracy of 93.01%,a macro-F1 of 88.54%,and an ROC–AUC of 98.44%,demonstrating strong robustness across ten test subsets.Evaluation on the more complex ISIC 2019 dataset further confirms the model’s effectiveness,where CANNSkin achieves 94.27%accuracy,93.95%precision,94.09%recall,and 99.02%F1-score,supported by high reconstruction fidelity(PSNR 35.03 dB,SSIM 0.86).These results demonstrate the effectiveness of our proposed latent-space balancing and fine-tuned representation learning as a new benchmark method for robust and accurate skin cancer classification across heterogeneous datasets. 展开更多
关键词 Computational image processing imbalance classification medical image analysis melanoma skin cancer classification
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Enhancing multiclass brain tumor classification through automated segmentation-guided deep learning 认领 引用
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作者 Pattaramon Vuttipittayamongkol Phakorn Charoenthiphakorn +2 位作者 Yarida Fuangfoo Pornnapha Na Phirot Thanawat Sanosiang 《Medical Data Mining》 CAS 2026年第2期15-33,共19页
Background:Accurate classification of brain tumors from Magnetic Resonance Imaging(MRI)is essential for clinical decision-making but remains challenging due to tumor heterogeneity.Existing approaches often focus solel... Background:Accurate classification of brain tumors from Magnetic Resonance Imaging(MRI)is essential for clinical decision-making but remains challenging due to tumor heterogeneity.Existing approaches often focus solely on classification or treat segmentation and classification as separate tasks,limiting overall performance and interpretability.Methods:This study proposes an end-to-end automated framework that integrates optimized tumor localization with multiclass classification.An optimized segmentation model is first employed to generate tumor masks,which are then overlaid on MRI scans to produce attention-enhanced inputs.These inputs are subsequently used to train a convolutional neural network(CNN)classifier.Experiments were conducted on a public dataset comprising 4,237 MRI scans across four categories:normal,glioma,meningioma,and pituitary tumors.Results:Three widely used segmentation models were systematically evaluated,with an optimized U-Net achieving the best performance(accuracy=0.9939,Dice=0.8893).Segmentation-guided classification consistently improved performance across six CNN architectures,with the most notable gains observed in heterogeneous tumor types such as glioma and meningioma.Among the classifiers,EfficientNet-V2 achieved the highest performance,with an accuracy of 0.9835,precision of 0.9858,recall of 0.9804,and F1-score of 0.9828.The framework was further validated on an independent external dataset,demonstrating consistent performance and robustness across diverse MRI sources.Conclusion:The proposed framework demonstrates strong potential for multiclass brain tumor classification by effectively combining segmentation and classification.This segmentation-driven approach not only enhances predictive accuracy but also improves interpretability,making it more suitable for clinical applications. 展开更多
关键词 brain tumor classification MRI segmentation segmentation-guided CNN multiclass classification tumor localization medical imaging
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A robust phylogenomic framework supports a revised intrafamilial classification of Urticaceae 认领 引用
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作者 Xiao-Gang Fu Jie Liu +8 位作者 Richard I.Milne Alex K.Monro Shui-Yin Liu Qin Tian Gregory W.Stull Amos Kipkoech Ting-Shuang Yi De-Zhu Li Zeng-Yuan Wu 《Plant Diversity》 SCIE CAS CSCD 2026年第2期289-306,共18页
Over the past decade,phylogenomics has significantly enhanced our understanding of relationships among numerous angiosperm lineages.However,comprehensive phylogenetic studies combining broad sampling of both genomic s... Over the past decade,phylogenomics has significantly enhanced our understanding of relationships among numerous angiosperm lineages.However,comprehensive phylogenetic studies combining broad sampling of both genomic sequences and taxa within the nettle family(Urticaceae)are still lacking.Here,we reconstructed the phylogeny of Urticaceae(345 species across 89% of accepted genera)using concatenated and coalescent analyses from plastome and nuclear ribosomal DNA sequences.Different plastid datasets and tree inference methods yielded a consistent phylogenetic backbone,with 98% of nodes achieving>90% bootstrap support—a significant improvement compared to 54% of nodes in the latest published phylogenetic study of Urticaceae.Plastid and nuclear phylogenetic relationships were largely congruent,with several exceptions that warrant further study.In the context of the updated phylogenetic relationships,we propose dividing the family into seven tribes that correspond to seven major clades or subclades,including a newly established tribe,Sarcochlamydeae stat.nov.Our phylogenetic analysis indicates that Debregeasia and Phenax are non-monophyletic.By combing morphological,molecular and distributional evidence,we describe a new genus Chiajuia gen.nov.Additionally,we propose synonymizing the following genera:Cypholophus(to Boehmeria),Haroldiella(to Pilea),Hemistylus,Neodistemon,Rousselia(all to Pouzolzia),Hesperocnide(to Urtica),and Pellionia(to Elatostema),while recognizing Elatostematoides,Gonostegia,Leptocnide,Margarocarpus,Scepocarpus,and Sceptrocnide as distinct genera.This robust phylogenomic framework and revised classification lays a foundation for future studies on the evolution and ecology of Urticaceae.The approach applied here may also serve as an important reference for other large plant families in angiosperms. 展开更多
关键词 Chiajuia Intrafamilial classification Phylogenomics Plastome Sarcochlamydeae Urticaceae
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A Quad-Step Approach to Uncertainty-Aware Deep Learning for Skin Cancer Classification 认领 引用
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作者 Hamzeh Asgharnezhad Pegah Tabarisaadi +2 位作者 Abbas Khosravi Roohallah Alizadehsani U.Rajendra Acharya 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1849-1874,共26页
Accurate skin cancer diagnosis is vital for early treatment and improved patient outcomes.Deep learning models have shown promise in automating skin cancer classification,yet challenges remain due to data scarcity and... Accurate skin cancer diagnosis is vital for early treatment and improved patient outcomes.Deep learning models have shown promise in automating skin cancer classification,yet challenges remain due to data scarcity and limited uncertainty awareness.This study presents a comprehensive evaluation of deep learning-based skin lesion classification with transfer learning and UQ on the HAM10000 dataset.We benchmark several pre-trained feature extractors(including Contrastive Language-Image Pre-training(CLIP)variants,ResNet50,DenseNet121,VGG16,EfficientNet-V2-Large,and ConvNeXt Large)combined with traditional classifiers such as SVM,XGBoost,and logistic regression.Multiple PCA settings(64,128,256,512)are explored,with LAION CLIP ViT-H/14 and ViT-L/14 at PCA-256 achieving the strongest baseline results.In the UQ phase,Monte Carlo Dropout(MCD),Ensemble,and Ensemble Monte Carlo Dropout(EMCD)are applied and evaluated using uncertainty-aware metrics(UAcc,USen,USpe,UPre).Ensemble methods with PCA-256 provide the best balance between accuracy and reliability.Further improvements are obtained through feature fusion of top-performing extractors at PCA-256.Finally,we propose a feature-fusion-based model trained with a Predictive Entropy(PE)loss function,which outperforms all prior configurations across both standard and uncertainty-aware evaluations,advancing trustworthy deep learning-based skin cancer diagnosis. 展开更多
关键词 Deep learning Machine learning Classification Uncertainty quantification Skin cancer
AgroGeoDB-Net: A DBSCAN-Guided Augmentation and Geometric-Similarity Regularised Framework for GNSS Field-Road Classification in Precision Agriculture 认领 引用
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作者 Fengqi Hao Yawen Hou +4 位作者 Conghui Gao Jinqiang Bai Gang Liu Hoiio Kong Xiangjun Dong 《Computers, Materials & Continua》 SCIE EI 2026年第6期1186-1213,共28页
Field-road classification,a fine-grained form of agricultural machinery operation-mode identification,aims to use Global Navigation Satellite System(GNSS)trajectory data to assign each trajectory point a semantic labe... Field-road classification,a fine-grained form of agricultural machinery operation-mode identification,aims to use Global Navigation Satellite System(GNSS)trajectory data to assign each trajectory point a semantic label indicating whether the machine is performing field work or travelling on roads.Existing methods struggle with highly imbalanced class distributions,noisy measurements,and intricate spatiotemporal dependencies.This paper presents AgroGeoDB-Net,a unified framework that combines a residual BiLSTM backbone with two tightly coupled innovations:(i)a Density-Aware Local Interpolator(DALI),which balances the minority road class via density-aware interpolation while preserving road-segment structure;and(ii)a geometry-aware training objective that couples a DBSCAN-weighted focal loss with a density-regularised KL divergence,ensuring that both classification and latent representations reflect local trajectory density.The workflow first converts each enriched GNSS point into a 14-dimensional motion-spatial descriptor,projects it into a compact latent space through a variational auto-encoder,and then applies a residual BiLSTM to model bidirectional temporal dependencies before a linear classifier produces point-wise field-road predictions.Experiments on wheat,corn,and paddy datasets show overall accuracies of 98.62%,95.46%,and 93.35%,with consistently stronger road class and overall performance than existing methods.Ablation studies further confirm that both the residual shortcut and DALI contribute positively,with DALI providing the greatest benefit for the minority road class.Tests on the unseen Harvester and Tractor datasets also demonstrate strong generalisation to previously unseen datasets.Taken together,the results show that AgroGeoDB-Net delivers reliable and scalable field-road classification from GNSS trajectories. 展开更多
关键词 Field-road classification imbalanced dataset spatiotemporal features deep learning
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Seismic event classification in North China based on machine learning 认领 引用
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作者 Yixiao Zhang Tingting Wang +1 位作者 Ruifeng Liu Zibo Wang 《Earthquake Science》 CAS CSCD 2026年第3期257-272,共16页
Automated classification of seismic events is critical for earthquake monitoring and explosion detection,particularly in tectonically active regions,such as North China,where the waveform features of earthquakes and e... Automated classification of seismic events is critical for earthquake monitoring and explosion detection,particularly in tectonically active regions,such as North China,where the waveform features of earthquakes and explosions are highly similar.This study compared feature-based machine learning(ML)and image-based deep learning(DL)methods in event-and stationlevel classification frameworks.The dataset consisted of 1,847 events and more than 43,000 vertical-component waveforms with two input types,40-dimensional feature vectors for ML and spectrogram images for DL.The results showed that the eventlevel models consistently outperformed the station-level models,achieving over 98%accuracy;the station-level models performed well above 94%.On the test set,the ML and DL models exhibited comparable performance;however,the ML models demonstrated better generalization and lower computational demands.In contrast,DL models required fewer manual interventions.The misclassification analysis revealed distinct error patterns across the model types,indicating potential complementarity.These findings highlight the importance of model choice based on the input type,data granularity,and generalization needs.Although DL models are well suited to automated processing,ML approaches provide more robust and efficient solutions for real-world deployment. 展开更多
关键词 seismic event classification machine learning XGBoost EfficientNet time-frequency analysis
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MTSCFNet:a novel framework for improving tree species classification in a subtropical forest using RGB,LiDAR-derived,and GF-2 data 认领 引用
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作者 Linlong Wang Huaiqing Zhang +5 位作者 Rurao Fu Kexin Lei Yang Liu Tingdong Yang Jing Zhang Xiaoning Ge 《Journal of Forestry Research》 SCIE EI CAS CSCD 2026年第3期135-158,共24页
Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)... Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)often suffer from insufficient feature representation and noise interference,particularly in subtropical forests with high species diversity,leading to increased classification errors.To address these challenges,we proposed the Multi-source Tree Species Classification Fusion Network(MTSCFNet),a novel deep learning framework that integrates RGB imagery,LiDAR-derived feature maps,and GF-2 satellite data through a modified UNet backbone,which incorporates a three-branch encoder and a Triple Branch Feature Fusion(TBFF)module within a middle fusion strategy.We evaluated the MTSCFNet in Chinese-fir mixed forests located in the Shanxia Forest Farm,Jiangxi Province,China.The results showed that:(1)MTSCFNet outperformed four baseline models,achieving Macro F1(0.78±0.01),Micro F1(0.93±0.01),Weighted F1(0.93±0.01),a Matthews correlation coefficient(MCC)(0.89±0.01),Cohen’sĸ(0.89±0.01),and mIoU(0.69±0.01),with respective improvements of 4.05%in Macro F1,1.89%in Micro F1,0.09%in Weighted F1,1.67%in MCC,1.64%in Cohen’sĸ,and 5.92%in Mean IoU over the second best model,SwinUNet;(2)Compared to the best two-source combinations(R+S,R+L),MTSCFNet achieved up to 1.50%,3.28%,3.42%,6.72%,6.76%,and 3.51%higher Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,and up to 8.11%,2.63%,2.88%,5.01%,4.99%,and 11.48%improvements over single-source inputs,while also exhibiting the lowest variability,indicating strong robustness;(3)Under different fusion strategies,MTSCFNet with middle fusion surpassed early and late fusion by up to 15.31%,3.74%,3.99%,7.66%,7.76%,22.33%and 24.13%,5.76%,6.20%,11.48%,11.57%,32.96%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,respectively,validating the effectiveness of feature-level multi-modal integration;(4)In cross-region transfer experiments,MTSCFNet demonstrated strong spatial generalizability,achieving average scores of 0.78(Macro F1),0.87(Micro F1),0.86(Weighted F1),0.59(MCC),0.59(Cohen’sĸ),and 0.68(mIoU),and outperformed SwinUNet by up to 38.80%,9.40%,18.58%,22.48%,26.17%,and 33.00%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU across varying forest densities.Overall,MTSCFNet offers a robust,accurate,and transferable solution for tree species classification in complex subtropical forest environments. 展开更多
关键词 Tree species classification Deep learning Multi-source remote sensing data Subtropical forest
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Graph Attention Networks for Skin Lesion Classification with CNN-Driven Node Features 认领 引用
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作者 Ghadah Naif Alwakid Samabia Tehsin +3 位作者 Mamoona Humayun Asad Farooq Ibrahim Alrashdi Amjad Alsirhani 《Computers, Materials & Continua》 SCIE EI 2026年第1期1964-1984,共21页
Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and ... Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems. 展开更多
关键词 Graph neural network image classification DermaMNIST dataset graph representation
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Federated Dynamic Aggregation Selection Strategy-Based Multi-Receptive Field Fusion Classification Framework for Point Cloud Classification 认领 引用
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作者 Yuchao Hou Biaobiao Bai +3 位作者 Shuai Zhao Yue Wang Jie Wang Zijian Li 《Computers, Materials & Continua》 SCIE EI 2026年第2期1889-1918,共30页
Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to priva... Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment. 展开更多
关键词 Point cloud classification federated learning multi-receptive field fusion dynamic aggregation
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Lightweight and Robust Cross-Domain Microseismic Signal Classification Framework with Bi-Classifier Adversarial Learning 认领 引用
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作者 Dingran Song Feng Dai +2 位作者 Yi Liu Hao Tan Mingdong Wei 《Engineering》 SCIE EI CSCD 2026年第1期267-283,共17页
Automatic identification of microseismic(MS)signals is crucial for early disaster warning in deep underground engineering.However,three major challenges remain for practical deployment,namely limited resources,severe ... Automatic identification of microseismic(MS)signals is crucial for early disaster warning in deep underground engineering.However,three major challenges remain for practical deployment,namely limited resources,severe noise interference,and data scarcity.To address these issues,this study proposes the lightweight and robust entropy-regularized unsupervised domain adaptation framework(LRE-UDAF)for cross-domain MS signal classification.The framework comprises a lightweight and robust feature extractor and an unsupervised domain adaptation(UDA)module utilizing a bi-classifier disparity metric and entropy regularization.The feature extractor derives high-level representations from the preprocessed signals,which are subsequently fed into two classifiers to predict class probability.Through three-stage adversarial learning,the feature extractor and classifiers progressively align the distributions of the source and target domains,facilitating knowledge transfer from the labeled source to the unlabeled target domain.Source-domain experiments reveal that the feature extractor achieves high effectiveness,with a classification accuracy of up to 97.7%.Moreover,LRE-UDAF outperforms prevalent industry networks in terms of its lightweight design and robustness.Cross-domain experiments indicate that the proposed UDA method effectively mitigates domain shift with minimal unlabeled signals.Ablation and comparative experiments further validate the design effectiveness of the feature extractor and UDA modules.This framework presents an efficient solution for resource-constrained,noise-prone,and data-scarce environments in deep underground engineering,offering significant promise for practical implementations in early disaster warning. 展开更多
关键词 Deep learning Microseismic classification Lightweight design Noise robustness Unsupervised domain adaptation
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Crack type classification of fissured sandstone using acoustic emission and Gaussian mixture modeling:Effects of fissure inclination angles 认领 引用
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作者 XUE Kang-sheng PU Hai +5 位作者 LU Yin-long CHEN Yan-long WU Yu LI Ming LIU De-jun XU Jun-ce 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第5期2166-2188,共23页
Accurate identification of crack types in rock masses is critical for understanding damage mechanisms and ensuring the structural safety of rock engineering.This study presents a novel unsupervised classification fram... Accurate identification of crack types in rock masses is critical for understanding damage mechanisms and ensuring the structural safety of rock engineering.This study presents a novel unsupervised classification framework based on Gaussian mixture modeling(GMM)for distinguishing acoustic emission(AE)signatures associated with different fracture modes in sandstone samples that contain prefabricated fissures at varying inclination angles.The frequency-domain characteristics of the AE signals were extracted using fast Fourier transform(FFT),while the RA-AF ratio(rise time/amplitude versus average frequency)parameter space was employed to characterize the crack mechanisms.To increase classification accuracy and model robustness,the Bayesian information criterion(BIC)was introduced to determine the optimal number of Gaussian components.Experimental results from uniaxial compression tests reveal that fissure inclination significantly affects crack evolution behavior:low-angle fissures favor shear and hybrid cracks,whereas high-angle fissures cause tensile failure.The proposed GMM-based method effectively identifies tensile,shear,and hybrid cracks with increased objectivity and accuracy,outperforming traditional empirical RA-AF thresholding techniques.This research provides a reliable and generalizable approach for AE signal classification,which presents theoretical insights and practical support for real-time monitoring,early warning,and structural health assessment in fractured rock masses. 展开更多
关键词 acoustic emission sandstone crack classification Gaussian mixture model(GMM) RA-AF analysis fissure inclination
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