From single- to multi-modal remote sensing imagery interpretation: a survey and taxonomy
Science China Information Sciences, ISSN: 1869-1919, Vol: 66, Issue: 4
2023
- 50Citations
- 38Captures
- 3Mentions
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Most Recent News
Special topic: Artificial intelligence innovation in remote sensing
Artificial Intelligence (AI) plays a growing role in remote sensing. In particular, during the last decade there has been an exponentially increasing interest in deep
Review Description
Modality is a source or form of information. Through various modal information, humans can perceive the world from multiple perspectives. Simultaneously, the observation of remote sensing (RS) is multimodal. We observe the world macroscopically through panchromatic, Lidar, and other modal sensors. Multimodal observation of remote sensing has become an active area, which is beneficial for urban planning, monitoring, and other applications. Despite numerous advancements in this area, there has still not been a comprehensive assessment that provides a systematic overview with a unified evaluation. Accordingly, in this survey paper, we first highlight the key differences between single- and multimodal RS imagery interpretation, then use these differences to guide our research survey of multimodal RS imagery interpretation in a cascaded structure. Finally, some potential future research directions are explored and outlined. We hope that this survey will serve as a starting point for researchers to review state-of-the-art developments and work on multimodal research.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85151424625&origin=inward; http://dx.doi.org/10.1007/s11432-022-3588-0; https://link.springer.com/10.1007/s11432-022-3588-0; http://sciencechina.cn/gw.jsp?action=cited_outline.jsp&type=1&id=7453584&internal_id=7453584&from=elsevier; https://dx.doi.org/10.1007/s11432-022-3588-0; https://link.springer.com/article/10.1007/s11432-022-3588-0
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know