The DOI matches its Crossref record.
Unmixing-based multisensor multiresolution image fusion
Abstract
Constrained and unconstrained algorithms of the multisensor multiresolution technique (MMT) are discussed. They can be applied to unmix low-resolution images using the information about their pixel composition from co-registered high-resolution images. This makes it possible to fuse the low- and high-resolution images for a synergetic interpretation. The constrained unmixing preserves all the available radiometric information of the low-resolution image. On the other hand, the unconstrained unmixing may be preferable in case of noisy data. An analysis of the MMT sensitivity to sensor errors showed that the strongest requirement is the accuracy of geometric co-registration of the data; the co-registration errors should not exceed 0.1-0.2 of the low-resolution pixel size. Applications of the constrained and unconstrained algorithms are illustrated on examples of unmixing and fusion of the multiresolution reflective and thermal bands of a real TM/LANDSAT image as well as of a simulated image of the future ASTER/EOS-AMI sensor.
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
Cited by
Aiazzi, B., Alparone, L., Baronti, S., & Garzelli, A. (2002) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2002.803623 · Cited by 855
A comparative analysis of image fusion methods
Wang, Z., Ziou, D., Armenakis, C., Li, D., & Li, Q. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2005.846874 · Cited by 852
Zhu, X., Cai, F., Tian, J., & Williams, T. (2018) · Remote Sensing · doi:10.3390/rs10040527 · Cited by 523
Zhan, W., Chen, Y., Zhou, J., Wang, J., Liu, W., Voogt, J., Zhu, X., Quan, J., & Li, J. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.12.014 · Cited by 371
Niu, Z. (2012) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.6.063507 · Cited by 289
Metadata
- Authors
- Zhukov, B., Oertel, D., Lanzl, F., & Reinhackel, G.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 1999-05
- Volume
- 37
- Issue
- 3
- Pages
- 1212-1226
- ISSN
- 0196-2892 (ISSN-L)
- OpenAlex
- W2031596845
- Cited by
- 481
- References
- 0
- FWCI
- 13.07
- 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: Unmixing-based multisensor multiresolution image fusion. https://app.cochat.ai/papers/10.1109/36.763276
- The paper
- Zhukov, B., Oertel, D., Lanzl, F., & Reinhackel, G. (1999). Unmixing-based multisensor multiresolution image fusion. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/36.763276
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.
