The DOI matches its Crossref record; 161 of the 161 references it cites were found in a registry or an index.
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art
Open accessAbstract
This article brings together the advances of multisource and multitemporal data fusion approaches with respect to the various research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references. More specifically, this work provides a bird's-eye view of many important contributions specifically dedicated to the topics of pansharpening and resolution enhancement, point cloud data fusion, hyperspectral and lidar data fusion, multitemporal data fusion, and big data and social media. In addition, the main challenges and possible future research in each area are outlined and discussed.
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
A Computer Movie Simulating Urban Growth in the Detroit Region
Tobler, W. R. (1970) · Economic Geography · doi:10.2307/143141 · Cited by 9,719
High-resolution mapping of global surface water and its long-term changes
Pekel, J.-F., Cottam, A., Gorelick, N., & Belward, A. S. (2016) · Nature · doi:10.1038/nature20584 · Cited by 5,513
(2010) · Cited by 4,411
Review Article Digital change detection techniques using remotely-sensed data
SINGH, A. (1989) · International Journal of Remote Sensing · doi:10.1080/01431168908903939 · Cited by 3,849
White, T. (2009) · Cited by 2,800
Mobile crowdsensing: current state and future challenges
Ganti, R., Ye, F., & Lei, H. (2011) · IEEE Communications Magazine · doi:10.1109/mcom.2011.6069707 · Cited by 2,054
Gao, F., Masek, J., Schwaller, M., & Hall, F. (2006) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2006.872081 · Cited by 1,968
Cited by
Deep learning in multimodal remote sensing data fusion: A comprehensive review
Li, J., Hong, D., Gao, L., Yao, J., Zheng, K., Zhang, B., & Chanussot, J. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102926 · Cited by 630
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
Hang, R., Li, Z., Ghamisi, P., Hong, D., Xia, G., & Liu, Q. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2969024 · Cited by 466
Self-citationVivone, G., Dalla Mura, M., Garzelli, A., Restaino, R., Scarpa, G., Ulfarsson, M. O., Alparone, L., & Chanussot, J. (2021) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.3019315 · Cited by 359
Remote sensing of soil degradation: Progress and perspective
Wang, J., Zhen, J., Hu, W., Chen, S., Lizaga, I., Zeraatpisheh, M., & Yang, X. (2023) · International Soil and Water Conservation Research · doi:10.1016/j.iswcr.2023.03.002 · Cited by 304
UAV & satellite synergies for optical remote sensing applications: A literature review
Alvarez-Vanhard, E., Corpetti, T., & Houet, T. (2021) · Science of Remote Sensing · doi:10.1016/j.srs.2021.100019 · Cited by 285
Metadata
- Authors
- Ghamisi, P., Gloaguen, R., Atkinson, P. M., Benediktsson, J. A., Rasti, B., Yokoya, N., Wang, Q., Hofle, B., Bruzzone, L., Bovolo, F., Chi, M., & Anders, K.
- Venue
- IEEE Geoscience and Remote Sensing Magazine
- Published
- 2019-03
- Volume
- 7
- Issue
- 1
- Pages
- 6-39
- ISSN
- 2168-6831 (ISSN-L)
- 2473-2397
- 2373-7468
- OpenAlex
- W2923136550
- Cited by
- 629
- References
- 161
- FWCI
- 35.51
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Ghamisi, P., Gloaguen, R., Atkinson, P. M., Benediktsson, J. A., Rasti, B., Yokoya, N., Wang, Q., Hofle, B., Bruzzone, L., Bovolo, F., Chi, M., & Anders, K. (2019). Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art. IEEE Geoscience and Remote Sensing Magazine. https://doi.org/10.1109/mgrs.2018.2890023
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
