Spatially adaptive smoothing parameter selection for markov random field based sub-pixel mapping of remotely sensed images

International Journal of Remote Sensing

Volumn 33, Issue 24, 2012, Pages 7886-7901

  Li, Xiaodong ^a,b   Du, Yun ^a   Ling, Feng ^a  

^a Wuhan National Laboratory for Optoelectronics   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85010540176     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2012.703347     Document Type: Article

References (25)

1. Adams, J.B., Smith, M.O. and Gillespie, A.R., 1993, Imaging spectroscopy: interpretation based on spectral mixture analysis. In Remote Geochemical Analysis: Elemental and Mineralogical Composition, C.M. Pieters and P. Englert (Eds.), pp. 145-166 (New York: Cambridge University Press).
2. Atkinson, P.M., 1997, Mapping sub-pixel boundaries from remotely sensed images. In Innovations in GIS 4, Z. Kemp (Ed.), pp. 166-180 (London: Taylor and Francis).
3. Atkinson, P.M., 2005, Sub-pixel target mapping from soft-classified, remotely sensed imagery. Photogrammetric Engineering and Remote Sensing,71, pp. 839-846.
4. Atkinson, P.M., 2009, Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study. International Journal of Remote Sensing,30, pp. 5293-5308.
5. Foody, G.M., 2006, Sub-pixel methods in remote sensing. In Remote Sensing Image Analysis: Including the Spatial Domain, S.M. de Jong and F.D. van der Meer (Eds.), pp. 47-49 (Dordrecht: Kluwer Academic Publishers).
6. Foody, G.M., Muslim, A.M. and Atkinson, P.M., 2005, Super-resolution mapping of the waterline from remotely sensed data. International Journal of Remote Sensing,26, pp. 5381-5392.
7. Ge, Y., Li, S.P. and Lakhan, V.C., 2009, Development and testing of a subpixel mapping algorithm. IEEE Transactions on Geoscience and Remote Sensing,47, pp. 2155-2164.
8. Geman, S. and Geman, D., 1984, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, pp. 721-741.
9. Kasetkasem, T., Arora, M. and Varshney, P., 2005, Super-resolution land cover mapping using a Markov random field based approach. Remote Sensing of Environment,96, pp. 302-314.
10. Li, X.D., Du, Y., Ling, F., Wu, S.J. and Feng, Q., 2011, Using a sub-pixel mapping model to improve the accuracy of landscape pattern indices. Ecological Indicators,11, pp. 1160-1170.
11. Ling, F., Du, Y., Xiao, F., Xue, H.P. and Wu, S.J., 2010, Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images. International Journal of Remote Sensing,31, pp. 5023-5040.
12. Ling, F., Li, W.B., Du, Y. and Li, X.D., 2011, Land cover change mapping at the subpixel scale with different spatial-resolution remotely sensed imagery. IEEE Geoscience and Remote Sensing Letters,8, pp. 182-186.
13. Ling, F., Xiao, F., Du, Y., Xue, H.P. and Ren, X.Y., 2008, Waterline mapping at the subpixel scale from remote sensing imagery with high-resolution digital elevation models. International Journal of Remote Sensing,29, pp. 1809-1815.
14. Makido, Y., Shortridge, A. and Messina, J.P, 2007, Assessing alternatives for modeling the spatial distribution of multiple land-cover classes at sub-pixel scales. Photogrammetric Engineering and Remote Sensing,73, pp. 935-943.
15. Mertens, K.C., De Baets, B., Verbeke, L.PC. and De Wulf, R.R., 2006, A sub-pixel mapping algorithm based on sub-pixel/pixel spatial attraction models. International Journal of Remote Sensing,27, pp. 3293-3310.
16. Mertens, K.C., Verbeke, L.PC., Ducheyne, E.I. and De Wulf, R.R., 2003, Using genetic algorithms in sub-pixel mapping. International Journal of Remote Sensing,24, pp. 4241-4247.
17. Muslim, A.M., Foody, G.M. and Atkinson, P.M., 2006, Localized soft classification for super-resolution mapping of the shoreline. International Journal of Remote Sensing,27, pp. 2271-2285.
18. Richards, J.A. and Jia, X.P., 2006, Remote Sensing Digital Image Analysis: An Introduction, 4th ed. (New York: Springer-Verlag).
19. Shen, Z.Q., Qi, J.G., and Wang, K., 2009, Modification of pixel-swapping algorithm with initialization from a sub-pixel/pixel spatial attraction model. Photogrammetric Engineering and Remote Sensing,75, pp. 557-567.
20. Tatem, A.J., Lewis, H.G., Atkinson, PM. and Nixon, M.S., 2001, Super-resolution target identification from remotely sensed images using a Hopfield neural network. IEEE Transactions on Geoscience and Remote Sensing,39, pp. 781-796.
21. Tatem, A.J., Lewis, H.G., Atkinson, PM. and Nixon, M.S., 2002, Super-resolution target identification from remotely sensed images using a Hopfield neural network. IEEE Transactions on Geoscience and Remote Sensing,39, pp. 781-796.
22. Tatem, A.J., Lewis, H.G., Atkinson, P.M. and Nixon, M.S., 2003, Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network. International Journal of Geographical Information Science,17, pp. 647-672.
23. Thornton, M.W., Atkinson, P.M. and Holland, D.A., 2006, Sub-pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using superresolution pixel-swapping. International Journal of Remote Sensing,27, pp. 473-491.
24. Tolpekin, V.A. and Stein, A., 2009, Quantification of the effects of land-cover-class spectral separability on the accuracy of Markov-random-field-based superresolution mapping. IEEE Transactions on Geoscience and Remote Sensing,47, pp. 3283-3297.
25. Verhoeye, J. and De Wulf, R.R., 2002, Land cover mapping at sub-pixel scales using linear optimization techniques. Remote Sensing of Environment,79, pp. 96-104.