The DOI matches its Crossref record; 103 of the 103 references it cites were found in a registry or an index.
Data Fusion and Remote Sensing: An ever-growing relationship
Abstract
Characterized by a certain focus on the heavily discussed topic of image fusion in its beginnings, sensor data fusion has played a significant role in the remote sensing research community for a long time. With this article, we aim to provide a short overview of established definitions, targeting a generalized understanding of the topic. In addition, a review of the state of the art of remote sensing data fusion research is given. By bringing together the conventional view expressed in the classical data fusion community and a review of current activities in the field of Earth observation, this article provides a holistic view of generic data fusion concepts and their applicability to the remote sensing domain.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Zadeh, L. A. (1965) · Information and Control · doi:10.1016/s0019-9958(65)90241-x · Cited by 67,855
Zadeh, L. A. (1996) · Cited by 46,844
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
A Mathematical Theory of Evidence
Shafer, G. (2020) · Princeton University Press eBooks · doi:10.2307/j.ctv10vm1qb · Cited by 11,711
A Mathematical Theory of Evidence
Shafer, G. (1976) · Princeton University Press eBooks · doi:10.1515/9780691214696 · Cited by 9,083
Rough Sets: Theoretical Aspects of Reasoning about Data
Pawlak, Z. (1991) · Our Digital Library (Warsaw University of Technology) · Cited by 8,343
Cited by
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400
Self-citationJoint Design of Communication and Sensing for Beyond 5G and 6G Systems
Wild, T., Braun, V., & Viswanathan, H. (2021) · IEEE Access · doi:10.1109/access.2021.3059488 · Cited by 508
Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation
Xie, Y., Tian, J., & Zhu, X. X. (2020) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2019.2937630 · Cited by 482
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
Li, X., Zhang, G., Cui, H., Hou, S., Wang, S., Li, X., Chen, Y., Li, Z., & Zhang, L. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102638 · Cited by 303
Metadata
- Authors
- Schmitt, M., & Zhu, X. X.
- Venue
- IEEE Geoscience and Remote Sensing Magazine
- Published
- 2016-12
- Volume
- 4
- Issue
- 4
- Pages
- 6-23
- ISSN
- 2168-6831 (ISSN-L)
- 2473-2397
- OpenAlex
- W2549994478
- Cited by
- 357
- References
- 103
- FWCI
- 27.74
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Data Fusion and Remote Sensing: An ever-growing relationship. https://app.cochat.ai/papers/10.1109/mgrs.2016.2561021
- The paper
- Schmitt, M., & Zhu, X. X. (2016). Data Fusion and Remote Sensing: An ever-growing relationship. IEEE Geoscience and Remote Sensing Magazine. https://doi.org/10.1109/mgrs.2016.2561021
Data sources
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.