ANALYZING the SHAPE CHARACTERISTICS of LAND USE CLASSES in REMOTE SENSING IMAGERY

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volumn 1, Issue , 2012, Pages 135-140

  Jiao, Limin ^a,b,c   Liu, Yaolin ^a,b,c  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c WUHAN UNIVERSITY   (China)

Author keywords

Image Classification; Image Segmentation; Land Use; Landscape Metrics; Shape Metrics

Indexed keywords

EID: 84885836223     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprsannals-I-7-135-2012     Document Type: Conference Paper

References (28)

1. Bauer, M. E., Cipra, J. E., Anuta, P. E., Etheridge, J. B., 1979. Identification and area estimation of agricultural crops by computer classification of LANDSAT MSS data, Remote Sensing of Environment.8(1), pp. 77-92.
2. Bauer, M. E., Doyle, J. K., Heinert, N. J., 2002. Impervious surface mapping using satellite remote sensing. Proceedings of IEEE Geoscience and Remote Sensing Symposium, Toronto, Canada, 24-28 June.
3. Benfield, S. L., Guzman, H. M., Mair, J. M., Young, J. A. T., 2007. Mapping the distribution of coral reefs and associated sublittoral habitats in Pacific Panama: A comparison of optical satellite sensors and classification methodologies. International Journal of Remote Sensing, 28(22), pp. 5047-5070.
4. Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I. Hetnen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing 58(3-4), 239-258.
5. Blaschke, T., 2010. Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), pp. 2-16.
6. Frohn, R. C., 2006. The use of landscape pattern metrics in remote sensing image Classification, International Journal of Remote Sensing, 27(10), pp. 2025-2032.
7. Frohn, R.C., 1998. Remote Sensing for Landscape Ecology: New Metric Indicators for the Monitoring, Modeling, and Assessment of Ecosystems. Lewis Publishers, Boca Raton, FL.
8. Gurney, C., 1981. The use of contextual information to improve land cover classification of digital remotely sensed data. International Journal of Remote Sensing, 2(4), pp. 379-388.
9. Herold, M., Scepan, J., Clarke, K. C., 2002. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses, Environment and Planning A, 34, pp. 1443-1458.
10. Ji, C.Y., 2000. Land use classification of remotely sensed data using Kohonen self-organizing feature map neural networks. Photogrammetric Engineering and Remote Sensing, 66, pp. 1451-1460.
11. Lewis, H.G., Cote, S., Tatnall, A.R.L., 1997. Determination of spatial and temporal characteristics as an aid to neural network cloud classification. International Journal of Remote Sensing, 18, pp. 899-915.
12. Malaviya, S., Munsi, M., Oinam, G., Joshi, P. K., 2010. Landscape approach for quantifying land use land cover change (1972-2006) and habitat diversity in a mining area in Central India (Bokaro, Jharkhand), Environmental Monitoring and Assessment, 170, pp. 215-229.
13. McGarigal, K., Marks, B. J., 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. Gen. Tech. Report PNW-GTR-351, USDA Forest Service, Pacific Northwest Research Station, Portland, OR.
14. Narumalani, S., Zhou, Y., Jelinkski, D. E., 1998. Utilizing geometric attributes of spatial information to improve digital image classification. Remote Sensing Review, 16, pp. 233-253.
15. Nunes, C., Auge, J. I., 1999. Land use and Land-Cover Implementation Strategy. IGBP, Stockholm.
16. Ozesmi, S. L., Bauer, M. E., 2002. Satellite remote sensing of wetlands. Wetlands Ecology and Management, 10(5), pp. 381-402.
17. Pacifici, F., Chini, M., Emery, W. J., 2009. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land use classification, Remote Sensing of Environment, 113, pp. 1276-1292.
18. Platt, R. V., Rapoza, L., 2008. An evaluation of an object-oriented paradigm for land use/land cover classification. The Professional Geographer, 60, pp. 87-100.
19. Read, J. M., Lam, N. S-.N., 2002. Spatial methods for characterising land cover and detecting land-cover changes for the tropics, International Journal of Remote Sensing, 23(12), pp. 2457-2474.
20. Russ, J. C., 2002. The Image Processing Handbook, fourth ed. CRC Press, Boca Raton, FL.
21. Salas, W. A., Boles, S. H., Frolking, S., Xiao, X. Li, C., 2003. The perimeter/area ratio as an index of misregistration bias in land cover change estimates. International Journal of Remote Sensing, 24, pp. 1165-1170.
22. Sonka, M., Hlavac, V., and Boyle, R., 1993. Image Processing, Analysis, and Machine Vision. Chapman and Hall, London.
23. Steele, B. M., 2000. Combining multiple classifiers: an application using spatial and remotely sensed information for land cover type mapping. Remote Sensing of Environment, 74, pp. 545-556.
24. Steele, B. M., Redmond, R. L., 2001. A method of exploiting spatial information for improving classification rules: application to the construction of polygon-based land cover maps. International Journal of Remote Sensing, 22, pp. 3143-3166.
25. Stuckens, J., Coppin, P. R. Bauer, M. E., 2000. Integrated contextual information with per-pixel classified for improved land cover classification. Remote Sensing of Environment, 71, pp. 282-296.
26. Thakur, R. S., Dikshit, O., 1997. Contextual classification with IRS LISS-II imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 52, pp. 92-100.
27. Walker, B. H., Steffen, W. L., 1996. GCTE science: objectives, structure and implementation. In: Walker, B. H., Steffen, W.L. (Eds.), Global Change and Terrestrial Ecosystems. Cambridge University Press, Cambridge, pp. 3-9.
28. Zhou, Y., Narumalani, S. Jelinski, D.E., 1995. Improving demote sensing derived land use/land cover classification with the aid of spatial information. ACSM/ASPRS Annual Convention & Exposition Technical Papers (Bethesda, MD: ACSM/ASPRS), 4, pp. 363-374.