Study of urban spatial patterns from SPOT panchromatic imagery using textural analysis

International Journal of Remote Sensing

Volumn 24, Issue 21, 2003, Pages 4137-4160

  Zhang, Q ^a   Wang, J ^a,b   Gong, P ^c   Shi, P ^b  

^a UNIVERSITY OF WESTERN ONTARIO   (Canada)

^b BEIJING NORMAL UNIVERSITY   (China)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATRIX ALGEBRA; STATISTICAL METHODS;

PANCHROMATIC IMAGERY; SPATIAL PATTERNS; TEXTURE FEATURES;

IMAGE ANALYSIS;

IMAGE CLASSIFICATION; REMOTE SENSING; SATELLITE IMAGERY; SPOT; STATISTICAL ANALYSIS; TEXTURE; URBAN REGION;

BEIJING; CHINA;

EID: 0344099114     PISSN: 01431161     EISSN: None     Source Type: Journal    
DOI: 10.1080/0143116031000070445     Document Type: Article

References (30)

1. ARZANDEH, S., 2001, Evaluation of radar and optical satellite imagery for wetland mapping and change detection in Walpole Island, Ontario. Unpublished Masters thesis. Department of Geography, The University of Western Ontario, Canada.
2. ARZANDEH, S., and WANG, J., 2002, Texture evaluation of Radarsat imagery for wetland mapping. Canadian Journal of Remote Sensing, 28, 653-666.
3. BARALDI, A., and PARMIGGIANI, F., 1995, An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing, 33, 293-304.
4. BARNSLEY, M., and BARR, S., 1996, Inferring urban land use from satellite sensor images using kernel-based spatial reclassification. Photogrammetric Engineering and Remote Sensing, 62, 949-958.
5. CONNERS, R. W., and HARLOW. C. A., 1980, A theoretical comparison of texture algorithms. IEEE Transactions On Pattern Analysis and Machine Intelligence, PAMI-2. 204-222.
6. FORSTER, B., 1985, An examination of some problems and solutions in monitoring urban areas from satellite platforms. International Journal of Remote Sensing, 6, 139-151.
7. GAUBATZ, P.. 1995. Changing Beijing. The Geographical Review, 85, 79-96.
8. GONG, P., and HOWARTH, P. J., 1990, The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. Photogrametric Engineering and Remote Sensing, 56, 67-73.
9. GONG, P., MARCEAU. D. J., and HOWARTH, P. J., 1992, A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data. Remote Sensing of Environment, 40, 137-151.
10. HAACK, B., BRYANT. N., and ADAMS. S., 1987, An assessment of Landsat MSS and TM data for urban and near-urban land-cover digital classification. Remote Sensing of Environment, 21, 201-213.
11. HALL-BEYER, M., 2000, GLCM texture: a tutorial. Available on line at http://www.ucalgary.ca/~mhallbey/texture/texture_tutorial.html (22 July 2002).
12. HARALICK, R. M., 1979, Statistical and structural approaches to texture. Proceedings of the IEEE, 67, 786-804.
13. HARALICK, R. M., SHANMUGAM, K., and DINSTEIN. I, 1973, Textural features for image classification. IEEE Transactions on System, Man, and Cybernetics, SMC-3, 610-621.
14. HARRIS, P. M., and VENTURA. S. J., 1995, The integration of geographic data with remotely sensed imagery to improve classification in an urban area. Photogrametric Engineering and Remote Sensing, 61, 993-998.
15. HORNSTRA, T. J., LEMMENS. M. J. P. M., and WRIGHT. G. L., 1999, Incorporating intra-pixel reflectance variability in the multispectral classification process of high-resolution satellite imagery of urbanised areas. Cartography, 28, 1-9.
16. KARATHANASSI, V., IOSSIFIDIS, CH., and ROKOS, D., 2000, A texture-based classification method for classifying built areas according to their density. International Journal of Remote Sensing, 21, 1807-1823.
17. MARCEAU, D. J., HOWARTH P. J., DUBOIS. J. M., and GRATTON, D. J., 1990, Evaluation of the grey-level co-occurrence matrix method for land-cover classification using SPOT imagery. IEEE Transactions on Geoscience and Remote Sensing, 28. 513-519.
18. MESEV, V., 1998, The use of census data in urban image classification. Photogrammetric Engineering and Remote Sensing, 64, 431-438.
19. PESARESI, M., 2000, Texture analysis for urban pattern recognition using fine-resolution panchromatic satellite imagery. Geographical and Environmental Modelling, 4, 43-64.
20. SHABAN, M. A., and DIKSHIT, O., 2001, Improvement of classification in urban areas by the use of textural features: the case study of Lucknow city, Uttar Pradesh. International Journal of Remote Sensing, 22, 565-593.
21. WANG, F., and ZHOU, Y., 1999, Modelling urban population densities in Beijing 1982-90: suburbanisation and its causes. Urban studies, 36, 271-287.
22. WANG, J., 1995, Application of the Linear Feature Detection System-LINDA to image segmentation from remotely sensed data. Remote Sensing Reviews, 13, 49-66.
23. WANG, J., and HOWARTH, P. J., 1991, Structural measures for linear feature pattern recognition from satellite imagery. Canadian Journal of Remote Sensing, 17, 294-303.
24. WEBSTER, C. J., 1996, Population and dwelling unit estimates from space. Third World Planning Review, 18, 155-176.
25. WELCH, R., 1982, Spatial resolution requirements for urban studies. International Journal of Remote Sensing, 3, 139-146.
26. WESZKA, J. S., DYER, C. R., and ROSENFELD, A., 1976, A comparative study of texture measures for terrain classification. IEEE Transactions on System, Man, and Cybernetics, SMC-6, 269-285.
27. Wu, L., 1986, A brief history of ancient Chinese city planning: in urbs er regio, (Kassel: Gesamthochschulbibliothek).
28. Wu, L., 1999, Rehabilitating the old city of Beijing: a project in the Ju'er Hutong neighbourhood. (Vancouver: UBC Press).
29. ZHANG, Q., and WANG, J., 2003, A rule-based urban land use inferring method for fine resolution multispectral imagery. Canadian Journal of Remote Sensing, (in press).
30. ZHANG, Q., WANG, J., PENG, X., GONG, P., and SHI P., 2002, Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data. International Journal of Remote Sensing, 23, 3057-3078.