The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Spatial/spectral endmember extraction by multidimensional morphological operations
Abstract
Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral 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
Cites
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,398
Image analysis and mathematical morphology
(1982) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(82)90078-8 · Cited by 8,151
Detection Theory: A User's Guide
Macmillan, N. A., & Creelman, C. D. (1991) · Cited by 5,415
Haralock, R. M., & Shapiro, L. G. (1991) · Addison-Wesley Longman Publishing Co., Inc. eBooks · Cited by 3,933
Image Analysis and Mathematical Morphology.
Diggle, P. J., & Serra, J. (1983) · Biometrics · doi:10.2307/2531038 · Cited by 3,894
A survey of thresholding techniques
Sahoo, P. K., Soltani, S., & Wong, A. K. C. (1988) · Computer Vision, Graphics, and Image Processing · doi:10.1016/0734-189x(88)90022-9 · Cited by 2,662
Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)
Green, R. O., Eastwood, M. L., Sarture, C. M., Chrien, T. G., Aronsson, M., Chippendale, B. J., Faust, J. A., Pavri, B. E., Chovit, C. J., Solis, M., et al. (1998) · Remote Sensing of Environment · doi:10.1016/s0034-4257(98)00064-9 · Cited by 1,851
Heinz, D. C., & Chein-I-Chang (2001) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.911111 · Cited by 1,847
Harsanyi, J. C., & Chang, C.-I. (1994) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.298007 · Cited by 1,517
N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data:
Winter, M. E. (1999) · SPIE Proceedings · doi:10.1117/12.366289 · Cited by 1,414
Cited by
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., & Chanussot, J. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2194696 · Cited by 2,836
Self-citationVertex component analysis: a fast algorithm to unmix hyperspectral data
Nascimento, J. M. P., & Dias, J. M. B. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2005.844293 · Cited by 2,669
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
Self-citationDeep Learning for Classification of Hyperspectral Data: A Comparative Review
Audebert, N., Le Saux, B., & Lefevre, S. (2019) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2019.2912563 · Cited by 686
Bioucas-Dias, J. M., & Figueiredo, M. A. T. (2010) · 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing · doi:10.1109/whispers.2010.5594963 · Cited by 658
Metadata
- Authors
- Plaza, A., Martinez, P., Perez, R., & Plaza, J.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2002-09
- Volume
- 40
- Issue
- 9
- Pages
- 2025-2041
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2102159812
- Cited by
- 517
- References
- 27
- FWCI
- 39.95
- 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: Spatial/spectral endmember extraction by multidimensional morphological operations. https://app.cochat.ai/papers/10.1109/tgrs.2002.802494
- The paper
- Plaza, A., Martinez, P., Perez, R., & Plaza, J. (2002). Spatial/spectral endmember extraction by multidimensional morphological operations. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2002.802494
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.
