International Journal of Image and Data Fusion最新文献_第3页

Automatic extraction of non-bifurcated lunar lineaments from Lunar Reconnaissance Orbiter (LRO) monochromatic images based on a Markov chain method 基于马尔可夫链方法从月球勘测轨道器（LRO）单色图像中自动提取非分叉月球线状物

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2023-11-27 DOI: 10.1080/19479832.2023.2281417

Zhen Wang, Gai Luo, Teng Hu, Zhizhong Kang, Yuancheng Cui, Yin Liang, Yuyu Wang

引用次数: 0

Enhancing WOFOST crop model with unscented Kalman filter assimilation of leaf area index 利用无香味卡尔曼滤波器同化叶面积指数，改进 WOFOST 农作物模型

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2023-11-24 DOI: 10.1080/19479832.2023.2287037

O. D. Belozerova

引用次数: 0

An effective IoT-assisted adaptive thresholding and hybrid deep learning model for rice blast fungal detection 用于稻瘟病真菌检测的有效物联网辅助自适应阈值和混合深度学习模型

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2023-11-22 DOI: 10.1080/19479832.2023.2277935

Vidhya M, Dahlia Sam, Rajavarman V.N

引用次数: 0

Satellite image fusion using cyclic spatio-spectral GAN model 利用循环时空-光谱 GAN 模型进行卫星图像融合

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2023-11-20 DOI: 10.1080/19479832.2023.2284776

M. Hammad, Tarek A. Mahmoud, A. Amein, T. Ghoniemy

引用次数: 0

A variational driven optimization framework for pansharpening of multispectral images 多光谱图像泛锐化的变分驱动优化框架

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2023-11-20 DOI: 10.1080/19479832.2023.2283521

Y. Ramakrishna, Richa Agrawal

引用次数: 0

Research on multi-source remote sensing image registration technology based on Baker mapping 基于Baker映射的多源遥感图像配准技术研究

International Journal of Image and Data Fusion Pub Date : 2023-11-09 DOI: 10.1080/19479832.2023.2278671

Li Ma, Lei Huang

{"title":"Research on multi-source remote sensing image registration technology based on Baker mapping","authors":"Li Ma, Lei Huang","doi":"10.1080/19479832.2023.2278671","DOIUrl":"https://doi.org/10.1080/19479832.2023.2278671","url":null,"abstract":"ABSTRACTTo address the issues of inaccurate estimation of registration parameters and high mismatch rate in feature based remote sensing image registration, a registration method based on global feature triangle similarity is proposed. This method utilizes the similarity principle of feature triangles to evaluate the global geometric similarity of matching feature points to eliminate mismatched points. In addition, due to the sensitivity of phase information in the frequency domain to spatial transformations and structural differences, as well as its robustness to lighting and noise, a phase structure consistency measurement method is proposed for developing feature point position adjustment strategies. The results indicate that the registration method proposed by the research institute achieved the lowest RMSE with a size of 1.51. In terms of IRMSE indicators, compared to the RANSAC measurement model, the PH SSIM measurement model has a mean decrease of 0.253. This indicates that the improved registration model proposed in the study has advantages in improving registration accuracy. The innovation of this study lies in constructing a matching feature point evaluation model to eliminate mismatched points, and proposing a remote sensing image registration method based on mismatch point removal and feature point position adjustment.KEYWORDS: Baker mappingregistration accuracymisalignment pointsfeature pointsRMSEPH-SSIM Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe research is supported by: Scientific Research and Innovation Team of Chongqing Youth Vocational & Technical College, Enterprise Software Application Digital Transformation Technology Service Team (No., CQYFUTD202207).","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135243971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital image processing for atmospheric monitoring at Colombian Andes 哥伦比亚安第斯山脉大气监测的数字图像处理

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2023-09-01 DOI: 10.1080/19479832.2023.2252817

Yhesly López, E. Pawelko, Daniel Nisperuza

{"title":"Digital image processing for atmospheric monitoring at Colombian Andes","authors":"Yhesly López, E. Pawelko, Daniel Nisperuza","doi":"10.1080/19479832.2023.2252817","DOIUrl":"https://doi.org/10.1080/19479832.2023.2252817","url":null,"abstract":"ABSTRACT As an alternative to the current technologies, we explored the feasibility of using low cost and massive use of digital cameras as photometric sensors to retrieve the atmospheric total optical depth (τ) in the urban area of a city in the Colombian Andes. This study proposes a simple way to estimate τ from digital processing of images of the Sun based on the Beer-Bouguer-Lambert law Langley’s linear fitting for the colour levels in channels red, green, and blue registered by the pixels of cameras’ sensors. From February to March 2022, the τ values retrieved from the images were correlated to the retrieved values from a solar spectral radiometer (SSR). We found that τ is sensible to the featured changes in the local atmosphere and to the cameras’ exposure parameters setup. Under conditions of partly clear sky, around 80% (r > 0.8) of the τ values from cameras showed a linear correspondence to those retrieved from SSR system. Its spectral dependency (τ _red < τ _green < τ _blue) is in accordance with the physical phenomena in light-atmosphere interaction. The results suggest that the methodology applied can be used for monitoring the atmosphere at any geographical location in the world.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41985302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of spectral indices-based downscaled land surface temperature in a humid subtropical city 基于光谱指数的亚热带湿润城市地表温度降尺度分析

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2023-09-01 DOI: 10.1080/19479832.2023.2252818

Anupam Pandey, Arun Mondal, S. Guha, P. K. Upadhyay, Rashmi, S. Kundu

{"title":"Analysis of spectral indices-based downscaled land surface temperature in a humid subtropical city","authors":"Anupam Pandey, Arun Mondal, S. Guha, P. K. Upadhyay, Rashmi, S. Kundu","doi":"10.1080/19479832.2023.2252818","DOIUrl":"https://doi.org/10.1080/19479832.2023.2252818","url":null,"abstract":"ABSTRACT The present study analyses the seasonal influence of error estimated in downscaled land surface temperatures (LSTs) in a humid subtropical city using Landsat 8 data of summer and winter seasons in 2021. Thermal sharpening (TsHARP) algorithm is one of the most frequently used downscaling techniques which is originally based on normalised difference vegetation index (NDVI). This study assesses the capability of the TsHARP technique with a separate combination of four selected spectral indices (modified normalised difference water index, normalised difference bareness index, normalised difference built-up index [NDBI], and NDVI), and by determining the root mean square error (RMSE) and mean error produced by the sharpened LST. Besides, sharpened LST has also been estimated by combining the four spectral indices. It is observed that NDBI provides the most effective output (RMSE is 1.11 [30 m], 1.05 [120 m], 1.02 [240 m], and 0.99 [480 m] in summer, whereas RMSE is 0.61 [30 m], 0.59 [120 m], 0.57 [240 m], and 0.56 [480 m] in winter). NDBI-based sharpened LST generates the best relationship (R = 0.565 in summer and R = 0.537 in winter) with surface features. Fallow land generates the best relationship (R = 0.512 in summer and R = 0.530 in winter) with sharpened LST. The summer season (R = 0.438) generates a better relationship between surface features and sharpened LST than the winter season (R = 0.409).","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46878403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Statistical modelling of digital elevation models for GNSS-based navigation gnss导航数字高程模型的统计建模

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2023-06-05 DOI: 10.1080/19479832.2023.2218376

Hiba Al-Assaad, C. Boucher, A. Daher, Ahmad Shahin, J. Noyer

{"title":"Statistical modelling of digital elevation models for GNSS-based navigation","authors":"Hiba Al-Assaad, C. Boucher, A. Daher, Ahmad Shahin, J. Noyer","doi":"10.1080/19479832.2023.2218376","DOIUrl":"https://doi.org/10.1080/19479832.2023.2218376","url":null,"abstract":"ABSTRACT Recently, smart mobility has become a important activity in transportation systems such as public, autonomous and shared transports. These systems require reliable navigation applications that lead to precise localisation and optimised route. The GPS system may face problems such as signal degradation caused by conical effects, affecting the reliability and accuracy of the signal, or signal loss in poor visibility environments. By using other sensors, the vehicle location system can overcome these GPS problems. This work focuses on the estimation of the inclination, which will be used to optimise the route planning for the EV or HEV especially in order to control the energy consumption. This paper presents a multi-sensor fusion method, based on GNSS, INS, OSM and DEM data fused using a non-linear particle filter, to estimate and improve the slopes of road segments. A new statistical modelling of the DEM errors related to the spatial sampling of elevation data is proposed. This method is based on the definition of a geometrical window, called Adjacent Sliding Window (ASW), which dynamically selects the elevation data in the vicinity of the road. The proposed method is evaluated in a suburban transport network. The experimental results show the benefits of the vehicle attitude and road slope estimation accuracies.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45971141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A large-scale remote sensing scene dataset construction for semantic segmentation 用于语义分割的大规模遥感场景数据集构建

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2023-04-10 DOI: 10.1080/19479832.2023.2199005

LeiLei Xu, Shanqiu Shi, Yujun Liu, Hao Zhang, Dan Wang, Lu Zhang, Wan Liang, Hao Chen

{"title":"A large-scale remote sensing scene dataset construction for semantic segmentation","authors":"LeiLei Xu, Shanqiu Shi, Yujun Liu, Hao Zhang, Dan Wang, Lu Zhang, Wan Liang, Hao Chen","doi":"10.1080/19479832.2023.2199005","DOIUrl":"https://doi.org/10.1080/19479832.2023.2199005","url":null,"abstract":"ABSTRACT As fuelled by the advancement of deep learning for computer vision tasks, its application in other fields has been boosted. This technology has been increasingly applied to the interpretation of remote sensing image, showing high potential economic and societal significance, such as automatically mapping land cover. However, the model requires a considerable number of samples for training, and it is now adversely affected by the lack of a large-scale dataset. Moreover, labelling samples is a time-consuming and laborious task, and a complete land classification system suitable for deep learning has not been established. This limitation hinders the development and application of deep learning. To meet the data needs of deep learning in the field of remote sensing, this study develops JSsampleP, a large-scale dataset for segmentation, generating 110,170 data samples that cover various categories of scenes within Jiangsu Province, China. The existing Geographical Condition Dataset (GCD) and Basic Surveying and Mapping Dataset (BSMD) in Jiangsu were fully utilised, significantly reducing the cost of labelling samples. Furthermore, the samples were subject to a rigorous cleaning process to ensure data quality. Finally, the accuracy of the dataset is verified using the U-Net model, and the future version will be optimised continuously.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43341264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0