Open Access
Issue |
J. Eur. Opt. Society-Rapid Publ.
Volume 21, Number 2, 2025
|
|
---|---|---|
Article Number | 31 | |
Number of page(s) | 18 | |
DOI | https://doi.org/10.1051/jeos/2025029 | |
Published online | 10 July 2025 |
- El Abady NF, Zayed HH, Taha M, An efficient technique for detecting document forgery in hyperspectral document images, Alexandria Eng. J. 85, 207–217 (2023). https://doi.org/10.1016/j.aej.2023.11.040. [Google Scholar]
- Tuya, Graph convolutional enhanced discriminative broad learning system for hyperspectral image classification, IEEE Access 10, 90299–90311 (2022). https://doi.org/10.1109/ACCESS.2022.3201537. [Google Scholar]
- Ma F, Liu SY, Yang FX, Xu GX, Piecewise weighted smoothing regularization in tight framelet domain for hyperspectral image restoration, IEEE Access 11, 1955–1969 (2023). https://doi.org/10.1109/ACCESS.2022.3233831. [Google Scholar]
- Yuen PWT, Richardson M, An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition, Imaging Sci. J. 58, 241–253 (2010). https://doi.org/10.1179/174313110X12771950995716. [Google Scholar]
- Lv WJ, Wang XF, Overview of hyperspectral image classification, J. Sens. 2020, 4817234 (2020). https://doi.org/10.1155/2020/4817234. [Google Scholar]
- Uddin MP, Manun MA, Hossain MA, PCA-based feature reduction for hyperspectral remote sensing image classification, IETE Tech Rev 38, 377–396 (2021). https://doi.org/10.1080/02564602.2020.1740615. [Google Scholar]
- Islam MT, Islam MR, Uddin MP, Ulhaq A, A deep learning-based hyperspectral object classification approach via imbalanced training samples handling, Remote Sens. 15, 3532 (2023). https://doi.org/10.3390/rs15143532. [Google Scholar]
- Rodarmel C, Shan J, Principal component analysis for hyperspectral image classification, Surv. Land Inf. Sci. 62, 115–122 (2002). [Google Scholar]
- Du Q, Chang CI, Segmented PCA-based compression for hyperspectral image analysis, in Proceedings of the Chemical and Biological Standoff Detection, Vol. 5268 (SPIE, Bellingham, WA, USA, 2004), 274–281. https://doi.org/10.1117/12.518835. [Google Scholar]
- Hyvärinen EOA, Karhunen J. Independent Component Analysis (Wiley, Hoboken NJ, 2001). ISBN:9780471221319. https://doi.org/10.1002/0471221317. [Google Scholar]
- Bakken S, Orlandic M, Johansen TA, The effect of dimensionality reduction on signature-based target detection for hyperspectral imaging, SPIE Opt. Eng. Appl. 111310L (2019). https://doi.org/10.1117/12.2529141. [Google Scholar]
- Falco N, Bruzzone L, Benediktsson JA, An ICA based approach to hyperspectral image feature reduction, Proc. IEEE Geosci. Remote Sens. Symp., 3470–3473 (2014). https://doi.org/10.1109/IGARSS.2014.6947229. [Google Scholar]
- Zhang ZY, Data Mining Found, in: Intell Paradig, 1st edn., edited by Holmes DE, Lakshmi C (Springer-Verlag, Berlin, Heidelberg, 2012). ISBN: 978-3-642-23241-1. [Google Scholar]
- Chen G, Qian SE, Evaluation and comparison of dimensionality reduction methods and band selection, Can J Remote Sens 34, 26–36 (2008). https://doi.org/10.5589/m08-007. [Google Scholar]
- Guan LX, Xie WX, Pei JH, Segmented minimum noise fraction transformation for efficient feature extraction of hyperspectral images, Pattern Recognit 48, 3216–3226 (2015). https://doi.org/10.1016/j.patcog.2015.04.013. [Google Scholar]
- Groves P, Bajcsy P, Methodology for hyperspectral band and classification model selection, in: IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (IEEE, Greenbelt, MD, 2003), pp. 120–128. https://doi.org/10.1109/WARSD.2003.1295183. [Google Scholar]
- Du Q, Yang H, Similarity-based unsupervised band selection for hyperspectral image analysis, IEEE Geosci. Remote Sens. Lett. 5(4, 564–568 (2008). https://doi.org/10.1109/LGRS.2008.2000619.. [Google Scholar]
- Su HJ, Sheng YH, Yang H, Du Q, Orthogonal projection divergence-based hyperspectral band selection, Spectroscopy Spectral Anal. 31(5), 1309–1313 (2011). https://doi.org/10.3964/j.issn.1000-0593(2011)05-1309-05. [Google Scholar]
- Su HJ, Du Q, Hyperspectral band clustering and band selection for urban land cover classification, Geocarto Int. 27(5), 395–411 (2012). https://doi.org/10.1080/10106049.2011.643322.. [Google Scholar]
- Sun WW, Zhang LP, Du B, Li WY, Mark Lai Y, Band selection using improved sparse subspace clustering for hyperspectral imagery classification, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2784–2797 (2015). https://doi.org/10.1109/JSTARS.2015.2417156. [Google Scholar]
- Sun WW, Peng JT, Yang G, Du Q, Correntropy-based sparse spectral clustering for hyperspectral band selection, IEEE Geosci. Remote Sens. Lett. 17(3), 484–488 (2020). https://doi.org/10.1109/LGRS.2019.2924934. [Google Scholar]
- Zhu B, Jin Y, Guan XH, Dong YN, SSMM: Semi-supervised manifold method with spatial-spectral self-training and regularized metric constraints for hyperspectral image dimensionality reduction, Int. J. Appl. Earth Obs. Geoinform. 136, 104373 (2025). https://doi.org/10.1016/j.jag.2025.104373. [Google Scholar]
- Du PJ, Wang XM, Tan K, Xia JS, Dimensionality reduction and feature extraction from hyperspectral remote sensing imagery based on manifold learning, Geomat. Inform. Sci Wuhan Univ 36(2), 148–152 (2011). [Google Scholar]
- Fang Y, Li H, Ma Y, Liang K, Hu YJ, Zhang SJ, Wang HY, Dimensionality reduction of hyperspectral images based on robust spatial information using locally linear embedding, IEEE Geosci. Remote Sens. Lett. 11(10), 1712–1716 (2014). https://doi.org/10.1109/LGRS.2014.2306689. [Google Scholar]
- Huang H, Shi GY, Duan YL, Zhang LM, Dimensionality reduction method for hyperspectral images based on weighted spatial-spectral combined preserving embedding, Acta Geod. Cartogr. Sin. 48(8), 1014–1024 (2019). [Google Scholar]
- Wang JL, Hou B, Jiao LC, Wang S, POL-SAR image classification based on modified stacked autoencoder network and data distribution, IEEE Trans. Geosci. Remote Sens. 58(3), 1678–1695 (2020). https://doi.org/10.1109/TGRS.2019.2947633. [Google Scholar]
- Tao C, Pan HB, Li YS, Zou ZR, Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification, IEEE Geosci. Remote Sens. Lett. 12(12), 2438–2442 (2015). https://doi.org/10.1109/LGRS.2015.2482520. [Google Scholar]
- Zhang MY, Gong MG, Mao YS, Li J, Wu Y, Unsupervised feature extraction in hyperspectral images based on Wasserstein generative adversarial network, IEEE Trans. Geosci. Remote Sens. 57(5), 2669–2688 (2019). https://doi.org/10.1109/TGRS.2018.2876123. [Google Scholar]
- Su HJ, Wu ZY, Zhang HH, Du Q, Hyperspectral anomaly detection: a survey, IEEE Geosci. Remote Sens. Mag. 10(1), 64–90 (2022). https://doi.org/10.1109/MGRS.2021.3105440. [Google Scholar]
- Tong QX, Zhang B, Zhen LF, Hyperspectral Remote Sensing: principles, techniques, and applications, edited by Chen ZX, 1st edn. (Higher Education Press, Beijing, 2006). [Google Scholar]
- Roger RE, Arnold JF, Reliably estimating the noise in AVIRIS hyperspectral images, Int. J. Remote Sens. 17, 1951–1962 (1996). https://doi.org/10.1080/01431169608948750. [Google Scholar]
- Rasti B, Ulfarsson MO, Ghamisi P, Automatic hyperspectral image restoration using sparse and low-rank modeling, IEEE Geosci Remote Sens Lett 14, 2335–2339 (2017). https://doi.org/10.1109/LGRS.2017.2764059. [Google Scholar]
- Mahmood A, Sears M, Per-pixel noise estimation in hyperspectral images, IEEE Geosci. Remote Sens. Lett. 19, 5503205 (2022). https://doi.org/10.1109/LGRS.2021.3064998. [Google Scholar]
- Liu J, Hou Z, Li W, Tao R, Orlando D, Li H, Multipixel anomaly detection with unknown patterns for hyperspectral imagery, IEEE Trans. Neural Netw. Learn. Syst. 33, 5557–5567 (2022). https://doi.org/10.1109/TNNLS.2021.3071026. [Google Scholar]
- Manolakis D, Lockwood R, Cooley T, Is there a best hyperspectral detection algorithm, Proc. SPIE 7334, 733402 (2009). https://doi.org/10.1117/12.816917. [Google Scholar]
- Kraut S, Scharf L, The CFAR adaptive subspace detector is a scale-invariant GLRT, IEEE Trans. Signal Process 47, 2538–2541 (1999). https://doi.org/10.1109/SSAP.1998.739333. [Google Scholar]
- Broadwater J, Chellappa R, Hybrid detectors for subpixel targets, IEEE Trans. Pattern Anal. Mach. Intell. 29, 1891–1903 (2007). https://doi.org/10.1109/TPAMI.2007.1104. [Google Scholar]
- Fan G, Ma Y, Mei X, Fan F, Huang J, Ma J, Hyperspectral anomaly detection with robust graph autoencoders, IEEE Trans. Geosci. Remote Sens. 60, 1–14 (2022). https://doi.org/10.1109/TGRS.2021.3097097. [CrossRef] [Google Scholar]
- Wang S, Wang X, Zhang L, Zhong Y, Auto-AD: Autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder, IEEE Trans Geosci Remote Sens 60, 1–14 (2022). https://doi.org/10.1109/TGRS.2021.3057721. [CrossRef] [Google Scholar]
- Lu P, Jiang XW, Zhang YS, Liu XB, Cai ZH, Jiang JJ, Plaza A, Spectral–spatial and superpixelwise unsupervised linear discriminant analysis for feature extraction and classification of hyperspectral images, IEEE Trans. Geosci. Remote Sens. 61, 1–15 (2023). https://doi.org/10.1109/TGRS.2023.3330474. [Google Scholar]
- Jiang X, Xiong L, Yan Q, Zhang Y, Liu X, Cai Z, Unsupervised dimensionality reduction for hyperspectral imagery via Laplacian regularized collaborative representation projection, IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022). https://doi.org/10.1109/LGRS.2022.3153041. [CrossRef] [Google Scholar]
- Jiang J, Ma J, Chen C, Wang Z, Cai Z, Wang L, SuperPCA: A superpixelwise PCA approach for unsupervised feature extraction of hyperspectral imagery, IEEE Trans. Geosci. Remote Sens. 56(8), 4581–4593 (2018). https://doi.org/10.1109/TGRS.2018.2828029. [Google Scholar]
- Zhang Y, Wang Y, Chen X, Jiang X, Zhou Y, Spectral–spatial feature extraction with dual graph autoencoder for hyperspectral image clustering, IEEE Trans. Circuit.Syst. Video Technol. 32(12), 8500–8511 (2022). https://doi.org/10.1109/TCSVT.2022.3196679. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.