Steady progress in the development of new remote sensing devices and remote sensing technologies has dramatically improved the ability to observe the Earth and its changes. For example, these improvements are due to higher spectral, spatial, temporal, and/or radiometric resolution, but also due to access to different types of sensors. However, the ever-increasing amount and variety of data also brings methodological challenges that require the development of more advanced pattern recognition techniques for efficient data interpretation.
The International Association for Pattern Recognition (IAPR) Technical Committee 7 (TC-7 Remote Sensing and Mapping) aims at promoting the use of pattern recognition methods in the analysis of data collected from satellites or airborne sensors used for Earth observation. The Pattern Recognition in Remote Sensing (PRRS) Workshop series, which has been organized by the IAPR TC7 and technically sponsored by the International Society for Photogrammetry and Remote Sensing (ISPRS) offers a great opportunity for researchers to gain a better understanding of various research topics in remote sensing that require contributions from the pattern recognition community, and has established itself as an important interdisciplinary event for scientists involved in the combined fields of pattern recognition and remote sensing.
Following the success of the 11th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2020), this special issue is to provide an updated snapshot of recent advances in the use of pattern recognition techniques to address the challenges in new-generation remote sensing data. It will consider revised and extended papers initially presented at the workshop, as well as other submissions in response to this open call for papers.
Topics (but are not limited to):
- Extraction, selection, learning, and reduction of features
- Deep learning for Earth observation data
- Active and transfer learning
- Target and anomaly detection
- Data fusion
- Multi-modal and multi-temporal analysis
- Nonlinear methods for pattern recognition
- Novel remote sensing applications of pattern recognition
- Ribana Roscher, University of Bonn, Germany (firstname.lastname@example.org)
- Gabriele Cavallaro, Forschungszentrum Jülich, Germany (email@example.com)
- Jie Shan, Purdue University, USA (firstname.lastname@example.org)
- Eckart Michaelsen, Fraunhofer IOSB, Germany (email@example.com)
- Uwe Stilla, Technical University of Munich, Germany (firstname.lastname@example.org)