CALL FOR PAPERS
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Special Issue on“Semantic Extraction and Fusion of Multimodal Remote Sensing Data: Algorithms and Applications”
Recently, the advanced sensor devices have boosted spaceborne and airborne imaging techniques towards
diversified development mode, enabling us to acquire a large amount of multi-source, multi-sensor, and multimodal data more easily. The diversity of data can enhance capabilities in a wide range of applications across thefields of remote sensing, computer vision, and land management. However, different modalities may lead tosignificant gaps in performance when methods and algorithms are designed and developed separately. Therefore,
how to effectively extract semantic information from multimodal data and further fuse them is still a greatchallenging task due to the vulnerable semantic information between-in changeable scenes or extremely similar
categories, complex noises from environmental conditions and instrumental sensors, physically and chemicallyatmospheric effects, and data degradation and distortion caused by illumination or topography changes. To thisend, developing and designing new semantic extraction approaches and fusion strategies is an effective solution
to enhance understanding and interpretation. Although some experimental work have exploited thesynchronization of multimodal streams to investigate and model the scene semantics, yet the ability of effectively
extracting and blending the semantic information across multi-modalities remains limited. This special issue aims
at promoting recent advances in algorithms and applications that contribute to semantic extraction and fusion for
multimodal remote sensing data, e.g., 2D gray images, 2D radar images (e.g., SAR), 3D grid optical images (e.g.,RGB, multispectral, hyperspectral data), 3D unstructured point cloud data (e.g., LiDAR), and social media data.
The broad topics of this special issue include but are not limited to:
New models and algorithms for multi-modal data processing and representation
Multi-modal data fusion, analysis, and interpretation
Effective feature extraction for multi-source or multi-temporal image matching and registration
Multi-modal data reconstruction and restoration
Hand-crafted or learning-based multi-modal feature representation and analysis
Fusion of grid optical data and unstructured point cloud data
Multi-modal feature fusion for anomaly, detection, object detection, and change detection
Multi-modal or cross-modal feature learning and retrieval
Novel benchmark multisource datasets
Novel applicational case studies with multi-modal datasets

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ScheduleJanuary 1, 2021:
June 30, 2021:

Submission system opening
Submission system closing

FormatAll submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Semantic Extraction
and Fusion of Multimodal Data” special issue manuscript type. Prospective authors should consult the site
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on paper
submission. All submissions must be formatted using the IEEE standard format (double column, single-spaced).
Please visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download atemplate for transactions. Please note that since January 1, 2020, IEEE J-STARS has become a fully open-accessjournal charging a flat publication fee $1,250 per paper.
Guest Editors
Danfeng Hong
Lianru Gao
Naoto Yokoya
Jocelyn Chanussot
Chen Chen

German Aerospace Center, Germany (danfeng.hong@dlr.de)Chinese Academy of Sciences, China (gaolr@radi.ac.cn)
The University of Tokyo & RIKEN AIP, Japan (yokoya@k.u-tokyo.ac.jp)
Univesité Grenoble Alpes, France (jocelyn.chanussot@gipsa-lab.grenoble-inp.fr)
University of North Carolina at Charlotte, USA. (chen.chen@uncc.edu)