Journal of Remote Sensing Special Issue
Deep Learning Meets Remote Sensing: Advances in Data-Driven Models
Journal of Remote Sensing, a Science Partner Journal, is now considering submissions for its third special issue, Deep Learning Meets Remote Sensing: Advances in Data-Driven Models.
Driven by the increasing availability of large-scale annotated data sets, efficient training strategies, and high-performance computational hardware, various deep learning-based solutions have recently been developed in a wide range of applications, including remote sensing image interpretation. In contrast with traditional methods that rely on handcrafted features, deep learning-based methods allow the acquisition of knowledge directly from data and thus extract much more abstract and semantic features with better representation capability. Furthermore, due to the compelling advantages of deep neural networks, deep learning has opened an appealing era for remote sensing and has been attracting more and more research interest.
This special issue is devoted to the publication of survey articles and research papers on new data-driven methods, algorithms, and architectures of deep learning for remote sensing, especially the work that addresses the field's challenges.
Potential topics of interest include but are not limited to the following:
- Deep learning-based methods for remote sensing image understanding (e.g., object detection and recognition, object tracking, scene classification, hyperspectral image classification, semantic segmentation, image retrieval, pan-sharpening, change detection, image super-resolution, image restoration, denoising, etc.)
- Deep learning algorithms and models (weakly-supervised learning, semi-supervised learning, unsupervised learning, few-shot learning, etc.) for remote sensing
- Deep learning-based transfer learning/metric learning for remote sensing
- Understanding of deep learning architectures/algorithms for remote sensing images
- Multidisciplinary applications through the integration of deep learning and remote sensing
- Novel benchmark datasets for remote sensing image understanding
November 15, 2022
Please indicate in your cover letter that your submission is intended for inclusion in the special issue, Deep Learning Meets Remote Sensing: Advances in Data-Driven Models. There are no submission fees and article processing charges (APCs) are waived through June 2023. Papers will be published online after acceptance.
Gui-Song Xia received a Ph.D. degree in image processing and computer vision from CNRS LTCI, Telecom ParisTech, Paris, France, in 2011. From 2011 to 2012, he was a Post-Doctoral Researcher with the Centre de Recherche en Mathematiques de la Decision (CEREMADE), CNRS, Paris-Dauphine University, Paris. He is currently a full professor and leading a research group in computer vision and photogrammetry (CAPTAIN) at Wuhan University. Xia also served as a Visiting Scholar at DMA, Ecole Normale Superieure (ENS-Paris), for two months in 2018. His current research interests include mathematical modeling of images, videos, and point clouds; structure from motion; perceptual grouping; and remote sensing imaging. His work has been granted the Outstanding Contribution Award by Remote Sensing Journal and was selected as one of the Best Paper Candidates at CVPR’2021. He is now serving as an Associate Editor for several journals, including Pattern Recognition, Signal Processing: Image Communication, EURASIP Journal on Image & Video Processing, and Journal of Remote Sensing (JRS), in addition to serving as guest editor for IEEE Transactions on Big Data (TBD), Pattern Recognition Letters, and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (IEEE JSTARS).
Gong Cheng received a B.S. from Xidian University, Xi’an, China, in 2007, and M.S. and Ph.D. degrees from Northwestern Polytechnical University, Xi’an, China, in 2010 and 2013, respectively. He is currently a Professor at Northwestern Polytechnical University, Xi’an, China. His main research interests are deep learning, computer vision, pattern recognition, and remote sensing image analysis. He has published more than 40 papers in top journals and at conferences, including Proceedings of the IEEE, IEEE T-PAMI, IEEE TIP, IEEE TGRS, IEEE T-CSVT, IEEE CVPR, ICCV, and IJCAI. Cheng received the IEEE GRSS Highest Impact Paper Award and the IEEE T-CSVT Best Paper Award in 2021. He is an associate editor of Journal of Remote Sensing (JRS), IEEE Geoscience and Remote Sensing Magazine (IEEE GRSM), and the IEEE Journal on Miniaturization for Air and Space Systems (J-MASS), as well as a guest editor for IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (IEEE JSTARS).
Jinchang Ren received B. Eng. in Computer Software, M.Eng. in Image Processing and Pattern Recognition, and Eng.D. in Computer Vision from Northwestern Polytechnical University (NWPU), China, in 1992, 1997, and 2000, respectively. He was also awarded the Ph.D. degree in Electronic Imaging and Media Communication from the University of Bradford, United Kingdom in 2009. He is currently a full Professor with the National Subsea Centre, Robert Gordon University, Aberdeen, U.K. Ren was a Lecturer, Senior Lecturer, and then Reader with the Strathclyde Hyperspectral Imaging Centre from Dec 2010 to Dec 2020. Before that, he worked at several universities in the UK, including University of Bradford, University of Surrey, Kingston University, and University of Abertay, Dundee. He is a Senior Member of IEEE and Fellow of the Higher Education Academy, UK. His research interests include: image processing and analysis; intelligent multimedia information processing; visual computing; computer vision; content-based image/video retrieval and understanding; machine learning, big data analytics, visual surveillance; motion estimation; hyperspectral imaging. Ren sits on the editorial boards of four international journals, including J. of the Franklin Institute, IEEE JSTARS, IET Image Processing, and Big Data Analytics.
Chen Chen received a Ph.D. degree from the Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX, USA, in 2016. He is currently an assistant professor at the Center for Research in Computer Vision, University of Central Florida. His research interests include computer vision, deep learning, and image and video processing. He has published over 80 papers in refereed journals and at conferences in these areas. Chen is an Associate Editor of Journal of Real-Time Image Processing and IEEE Journal on Miniaturization for Air and Space Systems.
Xiaofeng Li (Fellow, IEEE) received a B.S. degree in optical engineering from Zhejiang University, Hangzhou, China, in 1985, M.S. degree in physical oceanography from the First Institute of Oceanography, Qingdao, China, in 1992, and a Ph.D. degree in physical oceanography from North Carolina State University, Raleigh, NC, USA, in 1997. He is with the Institute of Oceanology, the Chinese Academy of Sciences. His research interests include synthetic aperture radar applications in oceanography and marine meteorology, artificial intelligence oceanography, big data, and satellite image processing. Dr. Li is an Associate Editor for IEEE Transactions on Geoscience and Remote Sensing and the International Journal of Remote Sensing. He is an Editorial Board Member of the International Journal of Digital Earth, Big Earth Data, and the Journal of Oceanology and Limnology. He is also the Executive Editor-in-Chief of the Journal of Remote Sensing (a Science Partner Journal).
Professor Mihai Datcu is Senior Scientist and Data Intelligence and Knowledge Discovery research group leader with the Remote Sensing Technology Institute (IMF) of DLR and Professor with the Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, UPB. From 1992 to 2002, he held an Invited Professor Assignment with the Swiss Federal Institute of Technology (ETH Zurich), Switzerland. From 2005 to 2013 he has been Professor holder of the DLR-CNES Chair at ParisTech, Paris Institute of Technology, Telecom Paris. His research interests include explainable and physics aware Artificial Intelligence, smart sensors design, and quantum machine learning with applications in Earth Observation. He is involved in AI and Big Data from Space, European Space Agency (ESA), NASA and national research programs and projects. He is a member of the ESA Big Data from Space Working Group and Visiting Professor with ESA's Φ-Lab. Datcu has received various awards and recognitions, including: Best Paper Award, IEEE Geoscience and Remote Sensing Society Prize, 2006; National Order of Merit with the rank of Knight, for outstanding international research results, awarded by the President of Romania, 2008; the Chaire d'excellence internationale Blaise Pascal 2017 for international recognition in the field of data science in earth observation; and the 2018 Ad Astra Award for Excellence in Science. He is an IEEE Fellow.