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Cited by 357

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

Schmitt, Michael, Zhu, Xiao Xiang·2016·IEEE Geoscience and Remote Sensing Magazine

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.

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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

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CoChat. (2026). Citation check: Data Fusion and Remote Sensing: An ever-growing relationship. https://app.cochat.ai/papers/10.1109/mgrs.2016.2561021
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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

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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.