The DOI matches its Crossref record.
A Fast Intensity–Hue–Saturation Fusion Technique With Spectral Adjustment for IKONOS Imagery
Abstract
Among various image fusion methods, intensity-hue-saturation (IHS) technique is capable of quickly merging the massive volumes of data. For IKONOS imagery, IHS can yield satisfactory "spatial" enhancement but may introduce "spectral" distortion, appearing as a change in colors between compositions of resampled and fused multispectral bands. To solve this problem, a fast IHS fusion technique with spectral adjustment is presented. The experimental results demonstrate that the proposed approach can provide better performance than the original IHS method, both in processing speed and image quality.
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
A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening
Yuan, Q., Wei, Y., Meng, X., Shen, H., & Zhang, L. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2794888 · Cited by 649
Metadata
- Authors
- Tu, T.-M., Huang, P. S., Hung, C.-L., & Chang, C.-P.
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2004-10
- Volume
- 1
- Issue
- 4
- Pages
- 309-312
- ISSN
- 1545-598X (ISSN-L)
- OpenAlex
- W2106891293
- Cited by
- 602
- References
- 0
- FWCI
- 26.54
- 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: A Fast Intensity–Hue–Saturation Fusion Technique With Spectral Adjustment for IKONOS Imagery. https://app.cochat.ai/papers/10.1109/lgrs.2004.834804
- The paper
- Tu, T.-M., Huang, P. S., Hung, C.-L., & Chang, C.-P. (2004). A Fast Intensity–Hue–Saturation Fusion Technique With Spectral Adjustment for IKONOS Imagery. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/lgrs.2004.834804
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.