A spatial dependence approach to retrieving industrial complexes from digital images

Professional Geographer

Volumn 56, Issue 3, 2004, Pages 381-393

  Bian, Ling ^a   Xie, Zhixiao ^b  

^a UNIVERSITY AT BUFFALO   (United States)

^b FLORIDA ATLANTIC UNIVERSITY   (United States)

Author keywords

Image retrieval; Semivariogram; Spatial dependence

Indexed keywords

AERIAL PHOTOGRAPH; IMAGE ANALYSIS; INDUSTRIAL DISTRICT; MAPPING; VARIOGRAM;

EID: 4143063713     PISSN: 00330124     EISSN: 14679272     Source Type: Journal    
DOI: 10.1111/j.0033-0124.2004.05603007.x     Document Type: Article

References (20)

1. Atkinson, P. M., and N. J. Tate. 2000. Spatial scale problems and geostatistical solutions: a review. The Professional Geographer 52 (4): 607-23.
2. Barnsley, M.J., L. Moller-Jensen, and S. L. Barr. 2001. Inferring urban land use by spatial and structural pattern recognition. In Remote sensing and urban analysis, ed. J. Donnay, M. J. Barnsley, and P. A. Longley, 115-44. London: Taylor and Francis.
3. Bian, L. 1997. Multiscale nature of spatial data in scaling up environmental models. In Scale in remote sensing and GIS, ed. D. A. Quattrochi and M. F. Goodchild, 13-26. New York: Lewis Publishers.
4. Bian, L., and S. J. Walsh. 1993. Scale dependencies of vegetation and topography in a mountainous environment of Montana. The Professional Geographer 45 (1): 1-11.
5. Curran, P. J., and P. M. Atkinson. 1998. Geostatistics and remote sensing. Progress in Physical Geography 22 (1): 61-78.
6. DeCola, L. 1989. Fractal analysis of a classified Land-sat scene. Photogrammetric Engineering and Remote Sensing 55 (5): 601-10.
7. Gamba, P., and F. Casciati. 1998. GIS and image understanding for near-real-time earthquake damage assessment. Photogrammetric Engineering and Remote Sensing 64(10): 987-94.
8. Haralick, R. M. 1979. Statistical and structural approaches to texture. Photogrammetiic Engineering and Remote Sensing 49 (1): 55-64.
9. Hofmeier, M. 1999. Issues of scale and texture in a content based image retrieval system. Master’s thesis, State University of New York at Buffalo.
10. Huang, J., R. Kumar, M. Mitra, W. Zhu, and R. Zabih. 1997. Spatial color indexing and applications. In Proceedings, IEEE conference on computer vision and patterns recognition, 762-68.
11. Isaaks, E. H., and R. M. Srivastava. 1989. An introduction to applied geostatistics. New York: Oxford University Press.
12. Lam, N. S. N., and D. Quattrochi. 1992. On the issues of scale, resolution, and fractal analysis in the mapping sciences. Professional Geographer 44 (1): 88-97.
13. Manjunath, B. S., and W. Y. Ma. 1996. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18 (8): 837-42.
14. Pesaresi, M. 2000. Texture analysis for urban pattern recognition using fine-resolution panchromatic satellite imagery. Geographical and Environmental Modeling 4 (1): 43-63.
15. Robert, A., and K. S. Richards. 1988. On the modeling of sand bedforms using the semivariogram. Earth Surface Process and Landfoivns 13:459-73.
16. Rossi, R. E., D. J. Mulla, A. G. Journel, and E. H. Franz. 1992. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecological Monographs 62:277-314.
17. Ryherd, S., and C. Woodcock. 1996. Combining spectral and texture data in the segmentation of remotely sensed images. Photogrammetric Engineering and Remote Sensing 62 (2): 181-94.
18. Sheikholeslami, G., A. Zhang, and L. Bian. 1999. A multi-resolution content-based retrieval approach for geographic images. Geolnformatica 3 (2): 109-39.
19. Webster, R. 1985. Quantitative spatial analysis of soil in die field. Advances in Soil Science 3:1-70.
20. Zhu, C., and X. Yang. 1998. Study of remote sensing image texture analysis and classification using wavelet. International Journal of Remote Sensing 19 (16): 3197-203.