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

Volumn 47, Issue 9, 2009, Pages 3283-3297

  Tolpekin, Valentyn A ^a   Stein, Alfred ^a  

^a UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

Class separability; Image classification; Markov random field (MRF); Superresolution mapping (SRM)

Indexed keywords

CLASS SEPARABILITY; CLASSIFICATION ACCURACY; EMPIRICAL DATA; ENERGY FUNCTIONS; FIELD BOUNDARIES; KAPPA STATISTIC; LAND COVER; LOCAL ENERGY; MAP IMAGE; MARKOV RANDOM FIELD (MRF); OPTIMAL VALUES; RESOLUTION IMAGES; SCALE FACTOR; SMOOTHNESS PARAMETER; SOFT CLASSIFICATION; SPECTRAL SEPARABILITY; SUB-PIXELS; SUPERRESOLUTION MAPPING; SUPERRESOLUTION MAPPING (SRM);

HIDDEN MARKOV MODELS; IMAGE ACQUISITION; IMAGE ANALYSIS; IMAGE CLASSIFICATION; LANDFORMS; OPTICAL RESOLVING POWER; OPTIMAL SYSTEMS; OPTIMIZATION; RELUCTANCE MOTORS; STRUCTURAL FRAMES;

PHOTOMAPPING;

COMPARATIVE STUDY; IMAGE CLASSIFICATION; IMAGE RESOLUTION; LAND COVER; MAPPING METHOD; MARKOV CHAIN; NUMERICAL METHOD; PERFORMANCE ASSESSMENT; PIXEL;

EID: 70349327849     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2009.2019126     Document Type: Article

References (36)

1. A. H. S. Solberg, T. Taxt, and A. K. Jain, "A Markov random field model for classification of multisource imagery," IEEE Trans. Geosci. Remote Sens., vol.34, no.1, pp. 100-113, Jan. 1996.
2. P. M. Atkinson, "Resolution manipulation and sub-pixel mapping," in Remote Sensing Image Analysis: Including the Spatial Domain. Norwell, MA: Kluwer, 2004, pp. 51-70.
3. A. J. Tatem, H. G. Lewis, P. M. Atkinson, and M. S. Nixon, "Super-resolution land cover mapping from remotely sensed imagery using a Hopfield neural network," in Uncertainty in Remote Sensing and GIS. Hoboken, NJ: Wiley, 2003, pp. 77-98.
4. A. J. Tatem, H. G. Lewis, P. M. Atkinson, and M. S. Nixon, "Super-resolution target identification from remotely sensed images using a Hopfield neural network," IEEE Trans. Geosci. Remote Sens., vol.39, no.4, pp. 781-796, Apr. 2001.
5. A. J. Tatem, H. G. Lewis, P. M. Atkinson, and M. S. Nixon, "Super-resolution land cover pattern prediction using a Hopfield neural network," Remote Sens. Environ., vol.79, no.1, pp. 1-14, Jan. 2002.
6. K. C. Mertens, L. P. C. Verbeke, E. I. Ducheyne, and R. R. De Wulf, "Using genetic algorithms in sub-pixel mapping," Int. J. Remote Sens., vol.24, no.21, pp. 4241-4247, 2003.
7. K. C. Mertens, B. De Baets, L. P. C. Verbeke, and R. R. De Wulf, "A sub-pixel mapping algorithm based on sub-pixel/pixel spatial attraction models," Int. J. Remote Sens., vol.27, no.15, pp. 3293-3310, Aug. 2006.
8. M. W. Thornton, P. M. Atkinson, and D. A. Holland, "Sub-pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super-resolution swapping," Int. J. Remote Sens., vol.27, no.3, pp. 473-491, Feb. 2006.
9. T. Kasetkasem, M. K. Arora, and P. K. Varshney, "Super-resolution land cover mapping using a Markov random field based approach," Remote Sens. Environ., vol. 96, no. 3/4, pp. 302-314, Jun. 2005.
10. A. Boucher, P. C. Kyriakidis, and C. Cronkite-Ratcliff, "Geostatistical solutions for super-resolution land cover mapping," IEEE Trans. Geosci. Remote Sens., vol.46, no.1, pp. 272-283, Jan. 2008.
11. A. Boucher and P. C. Kyriakidis, "Super-resolution land cover mapping with indicator geostatistics," Remote Sens. Environ., vol.104, no.3, pp. 264-282, Oct. 2006.
12. A. J. Tatem, H. G. Lewis, P. M. Atkinson, and M. S. Nixon, "Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network," Int. J. Geogr. Inf. Sci., vol.17, no.7, pp. 647-672, Oct./Nov. 2003.
13. P. H. Swain and S. M. Davis, Remote Sensing: The Quantitative Approach. New York: McGraw-Hill, 1978.
14. T. Kailath, "The divergence and Bhattacharayya distance measures in signal selection," IEEE Trans. Commun. Technol., vol.COM-15, no.1, pp. 52-60, Feb. 1967.
15. P. H. Swain and R. C. King, "Two effective feature selection criteria for multispectral remote sensing," in Proc. 1st Int. Joint Conf. Pattern Recog., Nov. 1973, pp. 536-540.
16. J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction, 4th ed. New York: Springer-Verlag, 2006.
17. P. W. Mausel, W. J. Kramber, and J. K. Lee, "Optimum band selection for supervised classification of multispectral data," Photogramm. Eng. Remote Sens., vol.56, no.1, pp. 55-60, 1990.
18. K. S. Schmidt and A. K. Skidmore, "Spectral discrimination of vegetation types in a coastal wetland," Remote Sens. Environ., vol.85, no.1, pp. 92-108, Apr. 2003.
19. G. D'Urso and M. Menenti, "Performance indicators for the statistical evaluation of digital image classification," ISPRS J. Photogramm. Remote Sens., vol.51, no.2, pp. 78-90, Apr. 1996.
20. P. M. Atkinson, M. E. J. Cutler, and H. Lewis, "Mapping sub-pixel proportional land cover with AVHRR imagery," Int. J. Remote Sens., vol.18, no.4, pp. 917-935, Mar. 1997.
21. J. C. Ju, E. D. Kolaczyk, and S. Gopal, "Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing," Remote Sens. Environ., vol.84, no.4, pp. 550-560, Apr. 2003.
22. J. Verhoeye and R. De Wulf, "Land cover mapping at sub-pixel scales using linear optimization techniques," Remote Sens. Environ., vol.79, no.1, pp. 96-104, Jan. 2002.
23. R. A. Schowengerdt, "On the estimation of spatial-spectral mixing with classifier likelihood functions," Pattern Recognit. Lett., vol.17, no.13, pp. 1379-1387, Nov. 1996.
24. S. Z. Li, Markov Random Field Modeling in Image Analysis. New York: Springer-Verlag, 2001.
25. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol.PAMI-6, no.6, pp. 721-741, Nov. 1984.
26. P. M. Atkinson, "Spatial statistics," in Spatial Statistics for Remote Sensing. Norwell, MA: Kluwer, 1999, p. 284.
27. S. B. Serpico and G. Moser, "Weight parameter optimization by the Ho-Kasyap algorithm in MRF models for supervised image classifica-tion," IEEE Trans. Geosci. Remote Sens., vol.44, no.12, pp. 3695-3705, Dec. 2006.
28. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing," Science, vol.220, no.4598, pp. 671-680, May 1983.
29. J. J. Settle and N. A. Drake, "Linear mixing and the estimation of ground cover proportions," Int. J. Remote Sens., vol.14, no. 6, pp. 1159-1177, 1993.
30. P. M. Atkinson, "Issues of uncertainty in super-resolution mapping and the design of an inter-comparison study," in Proc. 8th Int. Symp. Spatial Accuracy Assessment Natural Resources Environ. Sci., Shanghai, China, 2004, pp. 145-154.
31. R. Congalton, "A review of assessing the accuracy of classifications of remotely sensed data," Remote Sens. Environ., vol.37, no.1, pp. 35-46, Jul. 1991.
32. J. R. Schott, Remote Sensing: The Image Chain Approach. New York: Oxford Univ. Press, 2007.
33. J. Kuckartz, "Super resolution mapping using Markov random fields," B.Sc. thesis, Univ. Twente, Enschede, The Netherlands, 2007.
34. D. L. B. Jupp, A. H. Strahler, and C. E. Woodcock, "Autocorrelation and regularization in digital images. I. Basic theory," IEEE Trans. Geosci. Remote Sens., vol.26, no.4, pp. 463-473, Jul. 1988.
35. P. Fisher, "The pixel: A snare and a delusion," Int. J. Remote Sens., vol.18, no.3, pp. 679-685, 1997.
36. A. Boucher and P. C. Kyriakidis, "Integrating fine scale information in super-resolution land-cover mapping," Photogramm. Eng. Remote Sens., vol.73, no.8, pp. 913-921, 2007.