Earth Science Informatics最新文献_第10页

SpinelVA. A new perspective for the visual analysis and classification of spinel group minerals 尖晶石VA。尖晶石类矿物视觉分析和分类的新视角

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-03 DOI: 10.1007/s12145-024-01393-5

Antonella S. Antonini, Leandro Luque, Gabriela R. Ferracutti, Ernesto A. Bjerg, Silvia M. Castro, María Luján Ganuza

{"title":"SpinelVA. A new perspective for the visual analysis and classification of spinel group minerals","authors":"Antonella S. Antonini, Leandro Luque, Gabriela R. Ferracutti, Ernesto A. Bjerg, Silvia M. Castro, María Luján Ganuza","doi":"10.1007/s12145-024-01393-5","DOIUrl":"https://doi.org/10.1007/s12145-024-01393-5","url":null,"abstract":"Spinel group minerals, found within various rock types, exhibit distinct categorizations based on their host rocks. According to Barnes and Roeder (2001), these minerals can be classified into eight primary groups, each further subdivided into variable numbers of subgroups that can be related to a particular tectonic setting. This classification is based on the cations corresponding to the end-members of the spinel prism and is traditionally analyzed in this prismatic space or using projections of it. In this prismatic representation, several categories tend to overlap, making it impossible to determine which is the tectonic environment in that scenario. An alternative to solve this problem is to generate representations of these groups considering more attributes, making the most of the many values measured during the geochemical analysis. In this paper, we present SpinelVA, a visual exploration tool that integrates Machine Learning techniques and allows the identification of groups using the cations considered by Barnes and Roeder and some additional ones obtained from chemical analysis. SpinelVA allows us to know the tectonic environment of unknown samples by categorizing them according to the Barnes and Roeder classification. Additionally, SpinelVA integrates a collection of visual analysis techniques alongside the already used spinel prism projections and provides a set of interactions that assist geologists in the exploration process. Users can perform a complete data analysis by combining the proposed techniques and associated interactions.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling of snow and glacier melt dynamics in a mountainous river basin using integrated SWAT and machine learning approaches 利用 SWAT 和机器学习综合方法建立山区河流流域积雪和冰川融化动态模型

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-03 DOI: 10.1007/s12145-024-01397-1

Abhilash Gogineni, Madhusudana Rao Chintalacheruvu, Ravindra Vitthal Kale

{"title":"Modelling of snow and glacier melt dynamics in a mountainous river basin using integrated SWAT and machine learning approaches","authors":"Abhilash Gogineni, Madhusudana Rao Chintalacheruvu, Ravindra Vitthal Kale","doi":"10.1007/s12145-024-01397-1","DOIUrl":"https://doi.org/10.1007/s12145-024-01397-1","url":null,"abstract":"Modelling streamflow in snow-covered mountainous regions with complex hydrology and topography poses a significant challenge, particularly given the pronounced influence of temperature lapse rate (TLAPS) and precipitation lapse rate (PLAPS). The Present study area covers 54,990 km2 in the western Himalayas, including the Tibetan Plateau and the Indian portion of the USRB up to Bhakra Dam in Himachal Pradesh. In order to estimate the snowmelt and rainfall runoff contributions to the catchment, an integrated Soil and Water Assessment Tool (SWAT) model incorporates a Temperature Index with an Elevation Band approach. The uncertainty analysis of the SWAT model has been conducted using the Sequential Uncertainty Fitting algorithm (SUFI-2). Furthermore, machine-learning models such as Long Short-Term Memory (LSTM) neural networks and Random Forest (RF) are integrated with the SWAT model to enhance the accuracy of streamflow predictions resulting from snowmelt. The performance indices of a model for the monthly calibration period are R2 = 0.83, NSE = 0.82, P-BIAS = 2.3, P-factor = 0.82, and R-factor = 0.81. The corresponding values for the validation period are R^2 = 0.78, NSE = 0.77, P-BIAS = 5.7, P-factor = 0.72 and R-factor = 0.66. The results show that 63.08% of the Bhakra gauging station’s annual streamflow has attributed to snow and glacier melt. The highest snow and glacier melt occur from May to August, while the minimum is observed from November to February. Regarding snowmelt forecasting, the LSTM model outperforms the RF model with an R2 value of 0.86 and 0.85 during training and testing, respectively. Additionally, sensitivity analysis highlights that soil and groundwater flow parameters, specifically SOL_K, SOL_AWC, and GWQMN, are the most sensitive parameters for streamflow modelling. The study confirms the effectiveness of SWAT for water resource planning and management in the mountainous USRB.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementing a new Research Data Alliance recommendation, the I-ADOPT framework, for the naming of environmental variables of continental surfaces 实施新的研究数据联盟建议--I-ADOPT 框架，为大陆表面环境变量命名

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-02 DOI: 10.1007/s12145-024-01373-9

Coussot Charly, Braud Isabelle, Chaffard Véronique, Boudevillain Brice, Sylvie Galle

{"title":"Implementing a new Research Data Alliance recommendation, the I-ADOPT framework, for the naming of environmental variables of continental surfaces","authors":"Coussot Charly, Braud Isabelle, Chaffard Véronique, Boudevillain Brice, Sylvie Galle","doi":"10.1007/s12145-024-01373-9","DOIUrl":"https://doi.org/10.1007/s12145-024-01373-9","url":null,"abstract":"To improve data usage in an interdisciplinary context, a clear understanding of the variables being measured is required for both humans and machines. In this paper, the I-ADOPT framework, which decomposes variable names into atomic elements, was tested within the context of continental surfaces and critical zone science, characterized by a large number and variety of observed environmental variables. We showed that the I-ADOPT framework can be used effectively to describe environmental variables with precision and that it was flexible enough to be used in the critical zone science context. Variable names can be documented in detail while allowing alignment with other ontologies or thesauri. We have identified difficulties in modeling complex variables, such as those monitoring fluxes between different environmental compartments and for variables monitoring ratios of physical quantities. We also showed that, for some variables, different decompositions were possible, which could make alignments with other ontologies and thesauri more difficult. The precision of variable names proved inadequate for data discovery services and a non-standard label (SimplifiedLabel) had to be defined for this purpose. In the context of open science and interdisciplinary research, the I-ADOPT framework has the potential to improve the interoperability of information systems and the use of data from various sources and disciplines.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141531237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hyperspectral remote sensing image watermarking using discrete wavelet transform and forensic based investigation archimedes optimization 利用离散小波变换和基于阿基米德优化的法证调查进行高光谱遥感图像水印处理

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-02 DOI: 10.1007/s12145-024-01394-4

Minal Bodke, Sangita Chaudhari

{"title":"Hyperspectral remote sensing image watermarking using discrete wavelet transform and forensic based investigation archimedes optimization","authors":"Minal Bodke, Sangita Chaudhari","doi":"10.1007/s12145-024-01394-4","DOIUrl":"https://doi.org/10.1007/s12145-024-01394-4","url":null,"abstract":"Rapid advancement in satellite communication over the last decade have resulted in the widespread use of remote sensing images. Additionally, as satellite image transmission over the Internet has increased, secrecy concerns have also arisen. As a result, digitally transmitted images must have great imperceptibility and confidentiality. Multispectral images consist of multiple bands. It is very challenging to select the important spectral band for watermarking so that the structural and visual quality of the satellite Image can be retained. This work proposes an innovative blind watermarking model based on a hybrid optimization strategy performed with the following two processes: the embedding process and the extraction process. A novel hybrid optimization named FBIAO algorithm, which is the amalgamation of Archimedes Optimization (ArchOA) and Forensic Based Investigation Optimization (FBIO) algorithm is used to select spectral band for watermarking. The proposed novel FBIAO enhances the balances between the exploration and exploitation, boosts the solution diversity and improves the convergence of FBI based optimization for spectral band selection. The 3-level Discrete Wavelet Transform (DWT) is used to embed the watermark logo in the selected spectral band image and then position selection is applied to identify the location for embedding the watermark. Further, the watermark image is scrambled using Arnold Map technique to avoid the correlation between image pixel. The proposed method provides a peak signal-to-noise ratio (PSNR) in the range of 35.57 dB to 36.80 dB and, a structural similarity index (SSIM) between 0.91 to 0.93 without attack for six sample datasets. It provides robustness for different attacks and offers SSIM in between 0.6 to 0.87 and normalized Correlation (NC) in between 0.8 to 0.91 which is superior over traditional techniques.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam 采用多种数据驱动方法估算越南 Kone 河流域的日流量

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-07-01 DOI: 10.1007/s12145-024-01390-8

Tran Tuan Thach

{"title":"Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam","authors":"Tran Tuan Thach","doi":"10.1007/s12145-024-01390-8","DOIUrl":"https://doi.org/10.1007/s12145-024-01390-8","url":null,"abstract":"This paper presents deep learning using LSTM, machine learning employing RF and GB algorithms, and the rating curve (RC) that can be used for estimating daily streamflow at the outlet of river basins. The Kone River basin in Vietnam is selected as an example for demonstrating the ability of these approaches. Hydro-meteorological data, including rainfall at Vinh Kim as well as water level and streamflow at Binh Tuong, were collected in the long period from 1/1/1979 to 31/12/2018. Multiple approaches mentioned above are implemented and applied for estimating daily streamflow at Binh Tuong in the Kone River basin. Firstly, coefficients and hyper-parameters in each approach are carefully determined using available hydro-meteorological data from 1/1/1979 to 31/12/2009 and dimensional and dimensionless error indexes. The results revealed that deep learning using LSTM presents the most suitable performance of the observed streamflow, with correlation coefficient r and NSE being close unity, while RMSE and MAE are less than 1.5% of the observed magnitude of streamflow. The RC and machine learning employing RF and GB algorithms procedures acceptably the observed streamflow, with r and NSE varying between 0.77 and 0.98, and RMSE and MAE ranging from 0.4 to 6.0% of the observed magnitude of streamflow. Secondly, multiple approaches are also applied for estimating daily streamflow from 1/1/2010 to 31/12/2018, revealing consistent statistical characteristics of streamflow in the river basin. Finally, the impacts of input data on output streamflow are discussed.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance comparison of various machine learning models for predicting water quality parameters in the Chebika Zone of Central Tunisia 用于预测突尼斯中部切比卡区水质参数的各种机器学习模型的性能比较

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-06-29 DOI: 10.1007/s12145-024-01370-y

Mohamed Abdelhedi, Hakim Gabtni

{"title":"Performance comparison of various machine learning models for predicting water quality parameters in the Chebika Zone of Central Tunisia","authors":"Mohamed Abdelhedi, Hakim Gabtni","doi":"10.1007/s12145-024-01370-y","DOIUrl":"https://doi.org/10.1007/s12145-024-01370-y","url":null,"abstract":"This groundbreaking study pioneers the application of state-of-the-art machine learning algorithms to predict pivotal water parameters, specifically pH, water depth, and salinity. Rigorously evaluating four leading algorithms (Random Forest Regressor, MLP Regressor, Support Vector Machine, and XGB Regressor) leveraging a substantial dataset and employing comprehensive metrics, including R², MSE, MAE, and cross-validation scores.Results unequivocally demonstrate the exceptional performance of MLP Regressor and XGB Regressor, consistently outclassing other models in predicting pH, with remarkable R² values and minimal errors. MLP Regressor excels as the preeminent model for water depth prediction, while XGB Regressor leads in accurately predicting salinity. The study underscores the paramount importance of cross-validation in meticulously assessing model robustness and generalization capabilities.A distinctive feature of this research lies in its innovative approach, incorporating geographic localization data (longitude, latitude, and altitude) as exclusive inputs for all models. This strategic integration showcases the algorithms' unprecedented ability to predict water parameters based solely on geographical coordinates, underscoring the transformative potential of machine learning in revolutionizing water resource management.The implications extend far beyond its immediate focus, encompassing critical areas such as geophysical exploration, environmental monitoring, water quality management, and ecological conservation. By providing invaluable insights into the application of machine learning algorithms for predicting key water parameters, this study positions itself at the forefront of scientific contributions, setting a new standard for excellence in sustainable water resource utilization.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Standard precipitation-temperature index (SPTI) drought identification by fuzzy c-means methodology 用模糊 c-means 方法识别标准降水-温度指数 (SPTI) 旱情

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-06-29 DOI: 10.1007/s12145-024-01359-7

Zekâi Şen

{"title":"Standard precipitation-temperature index (SPTI) drought identification by fuzzy c-means methodology","authors":"Zekâi Şen","doi":"10.1007/s12145-024-01359-7","DOIUrl":"https://doi.org/10.1007/s12145-024-01359-7","url":null,"abstract":"Global warming and climate change impacts intensify hydrological cycle and consequently unprecedented drought and flood appear in different parts of the world. Meteorological drought assessments are widely evaluated by the concept of standardized precipitation index (SPI), which provides drought classification. Its application is based on the probabilistic standardization procedure, but in the literature, there is a confusion with the statistical standardization procedure. This paper provides distinctive differences between the two approaches and provides the application of a better method. As a novel approach, SPI classification is coupled with fuzzy clustering procedure, which provides drought evaluation procedure based on two variables jointly, precipitation and temperature, which is referred to as the standard precipitation-temperature index (SPTI). The final product is in the form of fuzzy c-means clustering in five clusters with exposition of annual drought membership degrees (MDs) for each cluster and resulting objective function. The application of the proposed fuzzy methodology is presented for the long-term annual precipitation and temperature records from New Jersey Statewide records.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving the resource modeling results using auxiliary variables in estimation and simulation methods 利用估算和模拟方法中的辅助变量改进资源建模结果

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-06-28 DOI: 10.1007/s12145-024-01383-7

Siavash Salarian, Behrooz Oskooi, Kamran Mostafaei, Maxim Y. Smirnov

{"title":"Improving the resource modeling results using auxiliary variables in estimation and simulation methods","authors":"Siavash Salarian, Behrooz Oskooi, Kamran Mostafaei, Maxim Y. Smirnov","doi":"10.1007/s12145-024-01383-7","DOIUrl":"https://doi.org/10.1007/s12145-024-01383-7","url":null,"abstract":"Mineral resource modeling is always accompanied by challenges. It is pivotal to increase accuracy and reduce modeling errors in resource modeling. This research aims at improving the resource modeling results using auxiliary variables for estimation and simulation processes. For this purpose, the Darreh-Ziarat iron ore deposit in the west of Iran is selected as a case study. The susceptibility obtained from the 3D inversion result of the magnetometry data is used as a secondary variable in the resource modeling. First, the Fe grade was estimated by utilizing simple kriging (SK) and sequential Gaussian simulation (SGS) techniques. Then, using the auxiliary variable, the Fe grade was estimated by the cokriging (CK) and sequential Gaussian co-simulation (SGCS) methods. Considering various cut-off Fe grades, the average grade of Fe and its resource (tonnage) were calculated, and their results were compared. The mean of kriging variance saw a decline from 0.81 in the SK method to 0.67 in the CK method. This slight decrease in variance can create a profound impact on the resource classification results. The results showed that the use of an auxiliary variable in resource modeling of Darreh-Ziarat led to a reduction in estimation error, an improvement in the classification of mineral resources, and an increase in the number of high-grade Fe blocks. Finally, Fe grade values at different elevation levels were calculated using the four mentioned methods. The results revealed a strong resemblance in shallow and deep parts, while the middle part, which is the high-grade zone, showed more differences.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative evaluation of spatiotemporal variations of surface water quality using water quality indices and GIS 利用水质指数和地理信息系统比较评估地表水水质的时空变化

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-06-28 DOI: 10.1007/s12145-024-01389-1

Aysenur Uslu, Secil Tuzun Dugan, Abdellah El Hmaidi, Ayse Muhammetoglu

{"title":"Comparative evaluation of spatiotemporal variations of surface water quality using water quality indices and GIS","authors":"Aysenur Uslu, Secil Tuzun Dugan, Abdellah El Hmaidi, Ayse Muhammetoglu","doi":"10.1007/s12145-024-01389-1","DOIUrl":"https://doi.org/10.1007/s12145-024-01389-1","url":null,"abstract":"There is a need for a comprehensive comparative analysis of spatiotemporal variations in surface water quality, particularly in regions facing multiple pollution sources. While previous research has explored the use of individual water quality indices (WQIs), there is limited understanding of how different WQIs perform in assessing water quality dynamics in complex environmental settings. The objective of this study is to evaluate the effectiveness of three WQIs (Canadian Council of Ministers of the Environment (CCME), National Sanitation Foundation (NSF) and System for Evaluation of the Quality of rivers (SEQ-Eau) and a national water quality regulation in assessing water quality dynamics. The pilot study area is the Acısu Creek in Antalya City of Turkey, where agricultural practices and discharge of treated wastewater effluents impair the water quality. A year-long intensive monitoring study was conducted includig on-site measurements, analysis of numerous physicochemical and bacteriological parameters. The CCME and SEQ-Eau indices classified water quality as excellent/good at the upstream, gradually deteriorating to very poor downstream, showing a strong correlation. However, the NSF index displayed less accuracy in evaluating water quality for certain monitoring stations/sessions due to eclipsing and rigidity problems. The regulatory approach, which categorized water quality as either moderate or good for different sampling sessions/stations, was also found less accurate. The novelty of this study lies in its holistic approach to identify methodological considerations that influence the performance of WQIs. Incorporating statistical analysis, artificial intelligence or multi-criteria decision-making methods into WQIs is recommended for enhanced surface water quality assessment and management strategies.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive feature selection for hyperspectral image classification based on Improved Unsupervised Mayfly optimization Algorithm 基于改进型无监督蜉蝣优化算法的高光谱图像分类自适应特征选择

IF 2.8 4区地球科学

Earth Science Informatics Pub Date : 2024-06-28 DOI: 10.1007/s12145-024-01378-4

Mohammed Abdulmajeed Moharram, Divya Meena Sundaram

{"title":"Adaptive feature selection for hyperspectral image classification based on Improved Unsupervised Mayfly optimization Algorithm","authors":"Mohammed Abdulmajeed Moharram, Divya Meena Sundaram","doi":"10.1007/s12145-024-01378-4","DOIUrl":"https://doi.org/10.1007/s12145-024-01378-4","url":null,"abstract":"Hyperspectral imaging has appeared as a vital tool in remote sensing science for its efficacy in effectively delineating regions of interest. However, the classification of hyperspectral images (HSI) encounters notable challenges, including the high dimensionality of highly correlated bands and the scarcity of training samples. Addressing these challenges is very essential by determining the most relevant bands, as well as the utilization of unlabelled training samples. In response to these issues, this study presents an unsupervised framework based on an enhanced Mayfly Optimization Algorithm (MOA) in order to select the most informative spectral bands. The enhanced MOA effectively identifies informative bands by leveraging the random solutions to explore the global search space, and enhance the solution diversity. On the other hand, leveraging the best experiences to boost the local search, efficiently attaining optimal solutions. This balanced exploration-exploitation strategy ensures the algorithm’s robustness and effectiveness in addressing the optimization problem. Ultimately, the proposed approach is demonstrated at the pixel-level hyperspectral image classification using two machine learning classifiers: Random Forest and Support Vector Machine. Thorough experimentation carried out on three benchmark hyperspectral datasets consistently confirms the effectiveness of the proposed approach.","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0