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International Journal of Image and Data Fusion - Taylor and Francis | 2025 Impact Factor: 1.5 | Cite Score:3.9 | SCOPUS Q2

International Journal of Image and Data Fusion With Cite Score

Cite Score and Journal Rank of International Journal of Image and Data Fusion

  • About: The International Journal of Image and Data Fusion is a peer-reviewed journal that focuses on the fusion of image and data from various sources to enhance information extraction and interpretation. It covers research and development in the areas of image fusion, data fusion, and their applications in fields such as remote sensing, medical imaging, and computer vision. The journal aims to advance methodologies and technologies that combine multiple data sources to improve accuracy, reliability, and utility in diverse applications
  • Objective:
    The primary objective of the journal is to disseminate cutting-edge research on image and data fusion techniques and their practical applications. It seeks to provide a platform for presenting innovative approaches to combining and analyzing data from different modalities, sensors, and sources. The journal aims to foster collaboration and knowledge exchange among researchers, practitioners, and industry professionals to drive advancements in fusion technologies and their applications
  • Interdisciplinary Approach:
    The International Journal of Image and Data Fusion adopts an interdisciplinary approach, encouraging contributions from various fields such as computer science, engineering, mathematics, and applied sciences. This approach ensures a comprehensive exploration of fusion techniques, integrating insights from different disciplines to address complex challenges in data and image fusion. By bridging multiple areas of expertise, the journal aims to provide robust solutions and advancements in fusion methodologies
  • Impact:
    The journal has a significant impact on both academic research and practical applications in the field of image and data fusion. It is widely cited by researchers and practitioners seeking to understand and implement advanced fusion techniques. The journals articles contribute to the development of new algorithms, technologies, and applications, influencing advancements in areas such as remote sensing, medical diagnostics, and artificial intelligence. It also serves as a valuable resource for educators and students involved in the study of fusion technologies
  • Significance:
    The International Journal of Image and Data Fusion plays a crucial role in advancing the field of fusion technologies. By publishing high-quality research, it helps address the challenges of integrating and analyzing data from multiple sources, leading to improved accuracy and insights. The journals focus on innovative methodologies and practical applications supports the development of advanced systems and solutions across various domains. Its interdisciplinary approach and commitment to excellence make it an essential resource for anyone engaged in image and data fusion research and applications.

  • Editor-in-Chief:  Jixian Zhang

  • Scope: The International Journal of Image and Data Fusion focuses on the integration and fusion of data from multiple sources, particularly in the context of imaging and remote sensing technologies. Its scope includes, but is not limited to:
  • Image Fusion: Techniques and algorithms for combining images from different sensors or modalities to enhance information extraction, including multispectral, hyperspectral, and multi-view image fusion.
  • Data Fusion: Methods for integrating data from various sources to improve accuracy and reliability, including sensor fusion, feature-level fusion, and decision-level fusion.
  • Remote Sensing: Application of image and data fusion techniques in remote sensing, including satellite and aerial imagery, for applications such as land cover classification, environmental monitoring, and disaster management.
  • Medical Imaging: Fusion of medical images from different imaging modalities (e.g., MRI, CT, PET) to support diagnosis, treatment planning, and personalized medicine.
  • Computer Vision: Use of image and data fusion techniques in computer vision tasks, such as object recognition, scene analysis, and visual tracking.
  • Signal Processing: Methods for processing and analyzing fused data, including filtering, enhancement, and feature extraction.
  • Multimodal Data Fusion: Integration of data from diverse modalities beyond imaging, such as textual, acoustic, or sensor data, to improve understanding and decision-making.
  • Algorithms and Techniques: Development and evaluation of algorithms for image and data fusion, including statistical methods, machine learning approaches, and optimization techniques.
  • Applications: Practical applications of image and data fusion in various domains, including agriculture, urban planning, security and surveillance, and autonomous systems.
  • Quality Assessment: Techniques for evaluating the quality and performance of image and data fusion processes, including metrics for assessing fusion accuracy and effectiveness.
  • Software and Tools: Development and use of software tools and platforms for implementing image and data fusion techniques, including open-source and commercial solutions.
  • Case Studies: Real-world case studies demonstrating the application of image and data fusion techniques in different industries and research fields.
  • The journal aims to provide a comprehensive platform for researchers, practitioners, and engineers to share advancements and innovations in the field of image and data fusion, fostering interdisciplinary collaboration and knowledge exchange.

  • Print ISSN:  19479824

    Electronic ISSN:  19479832

  • Abstracting and Indexing:  Scopus

  • Imapct Factor 2025:  1.5

  • Subject Area and Category:  Computer Science,Computer Science Applications,Earth and Planetary Sciences,Earth and Planetary Sciences (miscellaneous)

  • Publication Frequency:  

  • H Index:  38

  • Best Quartile:

    Q1:  

    Q2:  Earth and Planetary Sciences (miscellaneous)

    Q3:  

    Q4:  

  • Cite Score:  3.9

  • SNIP:  0.460

  • Journal Rank(SJR):  0.418