Daniel Tutu Bigdeli, Behnaz Borba, Eduardo Zilles Chen, E. Chen, Jie Chen, Wei Chen, Weihai Chen, Timo Krehl, Gunther Kumar, Brijesh Lamb, Anupama B. Lang, Stefan Li, Leida Li, Wenping Li, Xiaodong Liu, Xiaolong Madeiro
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引用次数: 0
摘要
《国际图像与数据融合杂志》的编辑们向以下审稿人表示衷心的感谢,感谢他们在2019年为杂志做出的宝贵贡献。Abdelkareem, Mohamed Altuntas, C. Bama, B. Sathya benoh, Daniel Tutu Bigdeli, Behnaz Borba, Eduardo Zilles Chen, Erxue Chen, Jie Chen, Wei Chen, Weihai Chen, Qiqin De Alban, Jose Don T. Drissia, T. K. Fonseca, Leila Fryskowska, Anna Gabriel Avina Cervantes, Juan Gao, Feng Ghandehari, Mehran Gulersoy, A.E. Hiray, Yogita V. Hong, Haoyuan胡祥云,黄朝正,洪永东,Ibarrola-Ulzurrun, Edurne Jenerowicz,姜俊军,姜俊军,焦鑫伟,王立成,Wolfgang国际图像与数据融合学报,2020,VOL. 11, NO. 1。1, i-ii https://doi.org/10.1080/19479832.2020.1715649
Acknowledgement to Reviewers of the International Journal of Image and Data Fusion in 2019
The editors of the International Journal of Image and Data Fusion wish to express their sincere gratitude to the following reviewers for their valued contribution to the journal in 2019. Abdelkareem, Mohamed Altuntas, C. Bama, B. Sathya Benefoh, Daniel Tutu Bigdeli, Behnaz Borba, Eduardo Zilles Chen, Erxue Chen, Jie Chen, Wei Chen, Weihai Chen, Yong Chiranjeevi, Karri Coelho, Leandro dos S. Dai, Qiqin De Alban, Jose Don T. Drissia, T. K. Fonseca, Leila Fryskowska, Anna Gabriel Avina Cervantes, Juan Gao, Feng Ghandehari, Mehran Gulersoy, A.E. Hiray, Yogita V. Hong, Haoyuan Hu, Xiangyun Hu, Zhaozheng Huang, Yongdong Hung, Kwok-Wai Ibarrola-Ulzurrun, Edurne Jenerowicz, Agnieszka Jiang, Junjun Jiang, Xinwei Jiao, Licheng Kainz, Wolfgang INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION 2020, VOL. 11, NO. 1, i–ii https://doi.org/10.1080/19479832.2020.1715649
期刊介绍:
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.).