International Journal of Image and Data Fusion

期刊简介：Free online access: Inaugural issue International Journal of Image and Data Fusion is a subscription-based journal focussing on image and data fusion for the method, technology and applications in geoinformatics . It provides a single source of information for a wide range of remote sensing 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. This journal focuses on the theories, methodologies and applications of image and data fusion from SAR (Synthetic Aperture Radar) data, LiDAR data and all types of optical images. It also encourages submission on a broad range of topics such as concept studies, new fusion techniques at different processing level, image and data fusion architectures, algorithms, and novel applications. Papers addressing fusion needs for data from new or planned platforms and sensors are specifically invited. The journal welcomes original research papers, review papers, research letters, technical articles and book reviews 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 Fusion and integration of remote sensing data and crowdsourced data, including social media data, flowing car data, cell phone data, OSM data, etc. Data Assimilation: fusing data with models Multi-source classification and information extraction Integration of satellite, airborne and terrestrial sensor systems Comprehensive quality control and evaluation techniques for data fusion Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) Big data processing and decision making services based on multi-platform, multi-source, multi-scale, multi-temporal data sets New digital technologies related to image and data fusion, including, but not limited to data simulation, immersive technologies such as AI, augmented reality, autonomous navigation, 3D real scene, etc. Data fusion applications in geographic-related fields such as topographic mapping, landscape mapping, GIS, and natural hazard monitoring, etc. Applications in fusion and statistics between geospatial information and the data from economy, humanities, census, etc. to solve complex societal problems, such as natural resources surveying and monitoring, information security, environmental risk, etc. IJIDF operates a double-anonymized peer review policy. All published research articles in this journal have undergone rigorous peer review, based on initial editor screening and anonymous refereeing by independent expert referees. STAR Taylor & Francis/Routledge are committed to the widest possible dissemination of its journals to non-profit institutions in developing countries. Our STAR initiative offers individual researchers in Africa, South Asia and many parts of South East Asia the opportunity to gain one month’s free online access to 1,300 Taylor & Francis journals. For more information, please visit the STAR website . Authors can choose to publish gold open access in this journal.

【译文】免费在线访问：创刊号《国际图像与数据融合杂志》是一本订阅式期刊，专注于地理信息学中的图像和数据融合方法、技术和应用。它为各种遥感图像和数据融合方法、开发、技术和应用提供单一信息源。图像和数据融合技术对于将卫星、机载和地面成像系统的多种来源结合起来，并将其与其他相关数据集集成以增强信息提取和决策制定非常重要。图像与数据融合旨在将多传感器、多时态、多分辨率、多平台的图像数据与地理空间数据、GIS、原位和其他统计数据集集成在一起，以改进信息提取，并提高信息的可靠性。这会带来更准确的信息，从而提供稳健的运营绩效，即增加信心、减少歧义并改进分类，从而实现基于证据的管理。该期刊重点关注 SAR（合成孔径雷达）数据、LiDAR 数据和所有类型的光学图像的图像和数据融合的理论、方法和应用。它还鼓励提交广泛的主题，例如概念研究、不同处理级别的新融合技术、图像和数据融合架构、算法和新颖应用。特别邀请解决来自新的或计划中的平台和传感器的数据融合需求的论文。该杂志欢迎图像和数据融合所有领域的原创研究论文、评论论文、研究信件、技术文章和书评，包括但不限于以下方面和主题：具有不同空间、光谱、时间分辨率的融合图像的自动配准/几何方面；相位信息；像素、特征和决策级融合算法和方法 遥感数据和众包数据的融合和集成，包括社交媒体数据、流动汽车数据、手机数据、OSM数据等 数据同化：数据与模型的融合 多源分类和信息提取 卫星、机载和地面传感器系统的集成 数据融合的综合质量控制和评估技术 融合时间数据集用于变化检测研究（例如土地覆盖/土地利用变化研究） 大数据处理和基于多平台、多源、多尺度、多时态数据集的决策服务 与图像和数据融合相关的新型数字技术，包括但不限于数据模拟、人工智能、增强现实、自主导航、3D实景等沉浸式技术等 数据融合在地形测绘、景观测绘、GIS、自然灾害监测等地理相关领域的应用 地理空间信息与经济、人文、人口普查等数据的融合和统计应用解决复杂的社会问题，如自然资源调查和监测、信息安全、环境风险等。IJIDF实行双匿名同行评审政策。该期刊上发表的所有研究文章都经过了严格的同行评审，这是基于最初的编辑筛选和独立专家审稿人的匿名审稿。 STAR Taylor & Francis/Routledge 致力于向发展中国家的非营利机构尽可能广泛地传播其期刊。我们的 STAR 计划为非洲、南亚和东南亚许多地区的个人研究人员提供了一个月免费在线访问 1,300 种 Taylor & Francis 期刊的机会。欲了解更多信息，请访问STAR网站。作者可以选择在该期刊上发表黄金开放获取。