Assessing alternatives for modeling the spatial distribution of multiple land-cover classes at sub-pixel scales

Photogrammetric Engineering and Remote Sensing

Volumn 73, Issue 8, 2007, Pages 935-943

  Makido, Yasuyo ^a   Shortridge, Ashton ^a   Messina, Joseph P ^a  

^a MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL EFFICIENCY; MATHEMATICAL MODELS; OPTICAL RESOLVING POWER; SIMULATED ANNEALING;

SPATIAL AUTOCORRELATION; SPATIAL RESOLUTION;

GEOGRAPHIC INFORMATION SYSTEMS;

ALGORITHM; DATA SET; LAND COVER; LANDSAT; MODELING; PIXEL; SATELLITE DATA; SPATIAL DISTRIBUTION;

EID: 34547579202     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.73.8.935     Document Type: Article

References (27)

1. Aarts, E., and J. Korst, 1989. Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing, John Wiley & Sons, Inc., New York.
2. Atkinson, P.M., 1997. Mapping sub-pixel boundaries from remote sensed images, Innovations in GIS 4 (Z. Kemp, editor), London, Taylor & Francis, pp. 166-180.
3. th International Conference on GeoComputation, 24-26 September 2001, Brisbane, Australia, URL: http://www.geocomputation.org/2001/papers/ atkinson.pdf (last date accessed: 20 May 2007).
4. Akinson, P.M., 2004. Super-resolution land-cover classification using the two-point histogram, GeoENV IV: Geostatistics for Environmental Applications (X. Sánchez-Vila, J. Carrera, and J. Gómez-Hernández, editors), Dordrecht, Kluwer, pp. 15-28.
5. Atkinson, P.M., 2005. Sub-pixel target mapping from soft-classified, remotely sensed imagery, Photogrammetric Engineering & Remote Sensing, 71(7):839-846
6. Bailey, T.C., and A.C. Gatrell, 1995. Interactive Spatial Data Analysis, Longman Scientific & Technical, Harlow Essex, England.
7. Boucher, A., and P.C. Kyriakidis, 2006. Super-resolution land-cover mapping with indicator geostatistic, Remote Sensing of Environment, 104(3):264-282.
8. Cardille, J.A., and M.G. Turner, 2002. Understanding landscape metrics - I, Learning Landscape Ecology: A Practical Guide to Concepts and Techniques, (S.E. Gergel and M.G. Turner, editors), Springer, New York, pp. 85-100.
9. Congalton, R.G., and K. Green, 1999. Assessing the Accuracy of Remote Sensed Data: Principles and Practices, Lewis Publishers, New York.
10. Foody, G.M., and D.P. Cox, 1994. Sub-pixel land-cover composition estimation using a linear mixture model and fuzzy membership functions, International Journal of Remote Sensing, 15(3): 619-631.
11. Foody, G.M., A.M. Muslim, and P.M. Atkinson, 2005. Super resolution mapping of the waterline from remotely sensed data, International Journal of Remote Sensing, 26:5381-5392.
12. Goodchild, M.A., 1986. Spatial Autocorrelation, CATMOG 47, Geo Books, Norwich, UK.
13. Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation, Oxford University Press, New York.
14. Griffith, D., 1982. Geometry and Spatial Interaction, Annals of the Association of American Geographers, 72(3):332-346.
15. Griffith, D., 1987. Spatial Autocorrelation: A Primer, AAG, Washington, D.C.
16. Kanellopoulos, I., A. Varfis, G.G. Wilkinson, and J. Megier, 1992. Land-cover discrimination in SPOT imagery by artificial neural network-A twenty class experiment, International Journal of Remote Sensing, 13(5):917-924.
17. Kerdiles, H., and M.O. Grondona, 1995. NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa, International Journal of Remote Sensing, 16(7):1303-1325.
18. Lillesand, T.M., R.W. Kiefer, and J.W. Chipman, 2004. Remote Sensing and Image Interpretation, Fifth edition, John Wiley & Sons, New York.
19. Makido, Y., and A.M. Shortridge, 2007. Weighting function alternatives for a sub-pixel allocation model, Photogrammetric Engineering & Remote Sensing, In Press.
20. Mertens, K.C., L.P.C. Verbeke, E.I. Ducheyne, and R.R. De Wulf, 2003. Using generic algorithms in sub-pixel mapping, International Journal of Remote Sensing, 24(21):4241-4247.
21. Muslim, A.M., G.M. Foody, and P.M. Atkinson, 2006. Localized soft classification for super-resolution mapping of the shoreline, International Journal of Remote Sensing, 27:2271-2285.
22. Tatem, A.J., H.G. Lewis, P.M. Atkinson, and M.S. Nixon, 2001. Super-resolution target identification from remotely sensed images using a Hopfield neural network, IEEE Transactions on Geoscience and Remote Sensing, 39:781-796.
23. Tatem, A.J., H.G. Lewis, P.M. Atkinson, and M.S. Nixon, 2002. Super-resolution land-cover pattern prediction using a Hopfield neural network, Remote Sensing of Environment, 79:1-14.
24. Tatem, A.J., H.G. Lewis, P.M. Atkinson, and M.S. Nixon, 2003. Increasing the spatial resolution of agricultural land-cover maps using a Hopfield neural network, International Journal of Geographical Information Science, 17(7):647-672.
25. Thornton, M.W., P.M. Atkinson, and D.A. Holland, 2006. Super-resolution mapping of rural land-cover features from fine spatial resolution satellite sensor imagery, International Journal of Remote Sensing, 27:473-491.
26. Tou, J.T., and R.C. Gonzalez, 1974. Pattern Recognition Principles, Reading, Massachusetts, Addison Wesley.
27. Verhoeye, J., and R. De Wulf, 2002. Land-cover mapping at sub-pixel scales using linear optimization techniques, Remote Sensing of Environment, 79:96-104.