The DOI matches its Crossref record.
An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data
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
Single image haze removal using dark channel prior
He, K., Sun, J., & Tang, X. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.168 · Cited by 7,601
Fattal, R. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360671 · Cited by 2,114
NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images
Ancuti, C. O., Ancuti, C., & Timofte, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00230 · Cited by 517
“They introduced Dark Channel Prior (DCP), an extension of the dark object [15].”
BackgroundApplication of Remote Sensing in Water Resource Management: The Case Study of Lake Trasimeno, Italy
Giardino, C., Bresciani, M., Villa, P., & Martinelli, A. (2010) · Water Resources Management · doi:10.1007/s11269-010-9639-3 · Cited by 187
Metadata
- Authors
- Chavez, P. S.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 1988-04
- Volume
- 24
- Issue
- 3
- Pages
- 459-479
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W1978125380
- Cited by
- 1,825
- References
- 0
- FWCI
- 8.41
- 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: An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. https://app.cochat.ai/papers/10.1016/0034-4257(88)90019-3
- The paper
- Chavez, P. S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(88)90019-3
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.
