Geostatistical classification for remote sensing: An introduction

Computers and Geosciences

Volumn 26, Issue 4, 2000, Pages 361-371

  Atkinson, P M ^a   Lewis, P ^b  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Classification; Geostatistics; Smoothing; Texture; Variogram

Indexed keywords

GEOSTATISTICS; IMAGE CLASSIFICATION; REMOTE SENSING; VARIOGRAM;

EID: 0034092755     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0098-3004(99)00117-X     Document Type: Article

References (52)

1. Augustin N.H., Mugglestone M.A., Buckland S.T. An autologistic model for the spatial distribution of wildlife. Applied Ecology. 33:(2):1996;339-347.
2. Barr, S., Barnsley, M., 2000. Reducing structural clutter in land cover classification of very high spatial resolution remotely sensed images for urban land use mapping. Computers & Geosciences 26 (4), 433-449.
3. Berberoglu, S., Lloyd, C.D., Atkinson, P.M., Curran, P.J., 2000. The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean. Computers & Geosciences 26 (4), 385-396.
4. Bohling G.C. GSLIB-style programs for discriminant analysis and regionalized classification. Computers & Geosciences. 23:(7):1997;725-761.
5. Bourgaullt G., Marcotte D., Legendre P. The multivariate (co) variogram as a spatial weighting function in classification methods. Mathematical Geology. 24:(5):1992;463-478.
6. Brunsdon, C., 2000. A Bayesian approach to schools catchment-based modelling. Geographical and Environmental Modelling (in press).
7. Carr J.R. Spectral and textural classification of single and multiple band digital images. Computers & Geosciences. 22:(8):1996;849-865.
8. Carr J.R., Miranda F.P. The semivariogram in comparison to the co-occurrence matrix for classification of image texture. IEEE Transactions on Geoscience and Remote Sensing. 36:(6):1998;1945-1952.
9. Chen Y.Q., Nixon M.S., Thomas D.W. On texture classification. International Journal of Systems Science. 28:(7):1997;669-682.
10. Chen K.S., Yen S.K., Tsay D.W. Neural classification of SPOT imagery through integration of intensity and fractal information. International Journal of Remote Sensing. 18:(4):1997;763-783.
11. Chica-Olmo, M., Arbarca-Hérnandez, F., 2000. Computing geostatistical image texture for remotely sensed data classification. Computers & Geosciences 26 (4), 373-383.
12. Curran P.J. The semi-variogram in remote sensing: an introduction. Remote Sensing of Environment. 24:(3):1988;493-507.
13. Dungan J. Spatial prediction of vegetation quantities using ground and image data. International Journal of Remote Sensing. 19:(2):1998;267-285.
14. Foody, G.M., 2000. Estimation of sub-pixel land cover composition in the presence of untrained classes. Computers & Geosciences 26 (4), 469-478.
15. Foody G.M., Campbell N.A., Trodd N.M., Wood T.F. Derivation and applications of probabilistic measures of class membership from the maximum likelihood classification. Photogrammetric Engineering and Remote Sensing. 58:(9):1992;1335-1341.
16. Goovaerts P. Geostatistics for Natural Resources Evaluation. 1997;Oxford University Press, Oxford.
17. Gower J.C. A general coefficient of similarity and some of its properties. Biometrics. 27:1971;857-871.
18. Haack, B., Bechdol, M., 2000. Integrating multisensor data and RADAR texture measures for land cover mapping. Computers & Geosciences 26 (4), 411-421.
19. Haralick R.M., Shanmugam K., Dinstein I. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics. 3:1973;610-621.
20. Haralick R.M., Shapiro L.G. Image segmentation techniques. Computer Vision, Graphics and Image Processing. 29:(1):1985;100-132.
21. Harf J., Davis J.C. Regionalization in geology by multivariate classification. Mathematical Geology. 22:(5):1990;573-588.
22. Herzfeld U.C. A method for seafloor classification using directional variograms, demonstrated for data from the western flank of the Mid-Atlantic Ridge. Mathematical Geology. 25:(7):1993;901-924.
23. Herzfeld U.C., Higginson C.A. Automated geostatistical seafloor classification-principles, parameters, feature vectors, and discrimination criteria. Computers Geosciences. 22:(1):1996;35-52.
24. Janssen L.L.F., Molenaar M. Terrain objects, their dynamics and their monitoring by the integration of GIS and remote sensing. IEEE Transactions on Geoscience and Remote Sensing. 33:(3):1995;749-758.
25. Jupp D.L.B. Modelling directional variance and variograms using geometric optics. Journal of Remote Sensing (China). 1:1997;94-101.
26. Khodja A., Mengue A. Improvement in the thematic contribution of an XS spot image using spatial processes and a segmentation method - application to the region of Lagdo (Northern Cameroon). International Journal of Remote Sensing. 17:(5):1996;879-886.
27. Lark R.M. Geostatistical description of texture on an aerial photograph for discriminating classes of land-cover. International Journal of Remote Sensing. 17:(11):1996;2115-2133.
28. Lemoigne J., Tilton J.C. Refining image segmentation by integration of edge and region data. IEEE Transactions on Geoscience and Remote Sensing. 33:(3):1995;605-615.
29. Lobo A., Chic O., Casterad A. Classification of Mediterranean crops with multisensor data - per-pixel versus per-object statistics and image segmentation. International Journal of Remote Sensing. 17:(12):1996;2385-2400.
30. Lloyd C.D., Atkinson P.M. The effect of scale-related issues on the geostatistical analysis of Ordnance Survey (R) digital elevation data at the national scale. Gomez-Hernandez J., Soares A., Froidevaux R. GeoENV II: Geostatistics for Environmental Applications. 1998;Kluwer, Dordrecht.
31. Matheron G. Les Variables Regionalisees et Leur Estimation. 1965;Masson, Paris.
32. Matheron, G., 1971. The Theory of Regionalized Variables and its Applications. Centre de Morphologie Mathématique de Fontainebleau. Ecole des Minesde Paris, Fascicule 5, Fontainebleau.
33. Miranda F.P., Carr J.R. Application of the semivariogram textural classifier (STC) for vegetation discrimination using SIR-B data of the Guiana Shield, Northwestern Brazil. Remote Sensing Reviews. 10:1994;155-168.
34. Miranda F.P., Fonseca L.E.N., Carr J.R., Taranik J.V. Analysis of JERS-1 (FUYO-1) SAR data for vegetation discrimination in Northwestern Brazil using the semivariogram textural classifier (STC). International Journal of Remote Sensing. 17:(17):1996;3523-3529.
35. Miranda F.P., Fonseca L.E.N., Carr J.R. Semivariogram textural classification of JERS-1 (Fuyo-1) SAR data obtained over a flooded area of the Amazonian rainforest. International Journal of Remote Sensing. 19:(3):1998;549-556.
36. Miranda F.P., Macdonald J.A., Carr J.R. Application of the semivariogram textural classifier (STC) for vegetation discrimination using SIR-B data of Borneo. International Journal of Remote Sensing. 13:(12):1992;2349-2354.
37. Moline G.R., Bahr J.M. Estimating spatial distributions of heterogenous subsurface characteristics by regionalized classification of electrofacies. Mathematical Geology. 27:(1):1995;3-22.
38. Oliver M.A., Webster R. A geostatistical basis for spatial weighting in multivariate classification. Mathematical Geology. 21:(1):1989;15-35.
39. Palubinskas G., Lucas R.M., Foody G.M., Curran P.J. An evaluation of fuzzy and texture-based approaches for mapping regenerating tropical forest classes from Landsat-TM data. International Journal of Remote Sensing. 16:(4):1995;747-759.
40. Raghu P.P., Poongodi R., Yegnanarayana B. A combined neural-network approach for texture classification. Neural Networks. 8:(6):1995;975-987.
41. Ramstein G., Raffy M. Analysis of the structure of radiometric remotely-sensed images. International Journal of Remote Sensing. 10:(6):1989;1049-1073.
42. Ryherd S., Woodcock C. Combining spectral and textural data in the segmentation of remotely sensed images. Photogrammetric Engineering and Remote Sensing. 62:(2):1996;181-194.
43. St-Onge B.A., Cavayas F. Estimating forest stand structure from high-resolution imagery using the directional variogram. International Journal of Remote Sensing. 16:(11):1995;1999-2021.
44. Schröder, M., Walessa, M., Rehrauer, H., Seidel, K., Datcu, M., 2000. Gibbs random field models: a toolbox for information extraction. Computers & Geosciences 26 (4), 423-432.
45. Smith, G.R., Woodward, J.C., Heywood, D.I., Gibbard, P.L., 2000. Interpreting pleistocene glacial features from SPOT HRV data using fuzzy techniques. Computers & Geosciences.
46. Thomas I.L., Benning V.M., Ching N.P. Classification of Remotely Sensed Images. 1987;Adam Hilger, Bristol.
47. van der Meer F. Classification of remotely-sensed imagery using an indicator kriging approach-application to the problem of calcite-dolomite mineral mapping. International Journal of Remote Sensing. 17:(6):1996;1233-1249.
48. van der Meer F. Geostatistical approaches for image classification and assessment of uncertainty in geologic processing. Atkinson P.M., Tate N.J. Advances in Remote Sensing and GIS Analysis. 1999;147-166 Wiley, Chichester.
49. Wallace, C.S.A., Watts, J.M., Yool, S.R., 2000. Characterizing the spatial structure of vegetation communities in the Mojave desert using geostatistical techniques. Computers & Geosciences 26 (4), 397-410.
50. Webster R., Burrough P.A. Computer-based soil mapping of small areas from sample data. II. Classification smoothing. Journal of Soil Science. 23:1972;222-234.
51. Woodcock C.E., Strahler A.H., Jupp D.L.B. The use of variograms in remote sensing I: Scene models and simulated images. Remote Sensing of Environment. 25:(3):1998;323-348.
52. Woodcock C.E., Strahler A.H., Jupp D.L.B. The use of variograms in remote sensing II: real digital images. Remote Sensing of Environment. 25:(3):1998;349-379.