A neighbourhood-constrained k-means approach to classify very high spatial resolution hyperspectral imagery

Remote Sensing Letters

Volumn 4, Issue 2, 2013, Pages 161-170

  Zhang, Bing ^a   Li, Shanshan ^a   Wu, Changshan ^b   Gao, Lianru ^a   Zhang, Wenjuan ^a   Peng, Man ^c  

^a CENTER FOR EARTH OBSERVATION AND DIGITAL EARTH   (China)

^b UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

^c INSTITUTE OF REMOTE SENSING APPLICATIONS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGERY; INTRA-CLASS VARIATION; K-MEANS; K-MEANS ALGORITHM; LOW SIGNAL-TO-NOISE RATIO; NEIGHBOURHOOD; SIMULATED IMAGES; SPATIAL AUTOCORRELATIONS; SPATIAL HETEROGENEITY; VERY-HIGH SPATIAL RESOLUTIONS;

REMOTE SENSING; SPECTROSCOPY;

ALGORITHMS;

ACCURACY ASSESSMENT; ALGORITHM; AUTOCORRELATION; IMAGE CLASSIFICATION; PERFORMANCE ASSESSMENT; PIXEL; SIGNAL-TO-NOISE RATIO; SPATIAL RESOLUTION; SPECTRAL ANALYSIS;

EID: 84869006110     PISSN: 2150704X     EISSN: 21507058     Source Type: Journal    
DOI: 10.1080/2150704X.2012.713139     Document Type: Article

References (15)

1. BANDYOPADHYAY, S. and MAULIK, U., 2002, An evolution technique based on K-means algorithm for optimal clustering in RN. Information Science, 146, pp. 221-237. (Pubitemid 35214300)
2. COCKS, T., JENSSEN, R., STEWART, A.,WILSON, I. and SHIELDS, T., 1998, The HyMap airborne hyperspectral sensor: the system, calibration and performance. In Proceedings of the 1st EASeL Workshop on Imaging Spectroscopy, 6-8 October 1998, Zürich, Switzerland (Paris: EARSeL), pp. 37-43.
3. HU, P.X., LU, Q.M., SHU, R. and WANG, J.Y., 2005, An airborne pushbroom hyperspectral imager with wide field of view (in Chinese). Chinese Optics Letters, 3, pp. 689-691. (Pubitemid 43320988)
4. JIMENEZ, L.O., RIVERA-MEDINA, J.L., RODRIGUEZ-DIAZ, E., ARZUAGA-CRUZ, E. and RAMIREZ-VELEZ, M., 2005, Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 844-851. (Pubitemid 40476028)
5. KESHAVA, N. andMUSTARD, J.F., 2002, Spectral unmixing. IEEE Signal Processing Magazine, 19, pp. 44-57. (Pubitemid 34237207)
6. KRAMER, H.J., 1996, Observation of the Earth and Its Environment: Survey of Missions and Sensors, 3rd ed. (Berlin: Springer-Verlag).
7. LUO, M., MA, Y.F. and ZHANG, H.J., 2003, A spatial constrained K-means approach to image segmentation. In Proceedings of the Joint Conference of the Fourth International Conference on Information, Communications and Signal Processing and the Fourth Pacific Rim Conference on Multimedia, 15-18 December 2003, Singapore, pp. 738-742.
8. MAITRA, R., 2009, Initializing partition-optimization algorithms. IEEE Transaction on Computational Biology and Bioinformatics, 6, pp. 144-157.
9. MIGNOTTE, M., 2011, A de-texturing and spatially constrained K-means approach for image segmentation. Pattern Recognition Letters, 32, pp. 359-367.
10. PLAZA, A., BENEDIKTSSON, J.A., BOARDMAN, J., BRAZILE, J., BRUZZONE, L., CAMPS-VALLS, G., CHANUSSOT, J., FAUVEL, M., GAMBA, P., GUALTIERI, J.A., MARCONCINI, M., TILTON, J.C. and TRIANNI, G., 2009, Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113, pp. S110-S122.
11. RICHARDS, J. and JIA, X., 2006, Remote Sensing Digital Image Analysis: An Introduction (Berlin: Springer-Verlag).
12. SOUCI, J.S., HANOU, I. and PUCHALSKI, D., 2009, High-resolution remote sensing image analysis for early detection and response planning for emerald ash borer. Photogrammetric Engineering and Remote Sensing, 75, pp. 905-909.
13. TADJUDIN, S. and LANDGREBE, D., 1998, Classification of high dimensional data with limited training samples. PhD thesis, Purdue University, USA.
14. WENG, Q., HU, X. and LU, D., 2008, Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison. International Journal of Remote Sensing, 29, pp. 3209-3232. (Pubitemid 351911288)
15. WULDER, M., FRANKLIN, S.E. and WHITE, J.C., 2004, Sensitivity of hyperclustering and labeling land cover to Landsat image acquisition data. International Journal of Remote Sensing, 25, pp. 5337-5344. (Pubitemid 40008574)