International Journal of Image and Data Fusion

International Journal of Image and Data Fusion
影响因子:
1.8
ISSN:
print: 1947-9832
on-line: 1947-9824
研究领域:
REMOTE SENSING-
自引率:
0.00%
Gold OA文章占比:
7.84%
原创研究文献占比:
100.00%
SCI收录类型:
Emerging Sources Citation Index (ESCI) || Scopus (CiteScore)
期刊介绍英文:
International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).
CiteScore:
CiteScoreSJRSNIPCiteScore排名
5.00.4730.948
学科
排名
百分位
大类:Earth and Planetary Sciences
小类:General Earth and Planetary Sciences
45 / 195
77%
大类:Computer Science
小类:Computer Science Applications
312 / 817
61%
发文信息
WOS期刊分区
学科分类
Q3REMOTE SENSING
历年影响因子
2022年2.3000
2023年1.8000
历年发表
2012年23
2013年25
2014年22
2015年25
2016年19
2017年21
2018年16
2019年18
2020年26
2021年26
2022年10
投稿信息
出版国家(地区):
United Kingdom
出版商:
Taylor & Francis

International Journal of Image and Data Fusion - 最新文献

Transfer learning by VGG-16 with convolutional neural network for paddy leaf disease classification

Pub Date : 2024-07-15 DOI: 10.1080/19479832.2024.2332365 R. Elakya, T. Manoranjitham

Urban heat island distribution observation by integrating remote sensing technology and deep learning

Pub Date : 2024-05-16 DOI: 10.1080/19479832.2024.2354754 Huanuan Lin
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