Urban slum detection using texture and spatial metrics derived from satellite imagery

Journal of Spatial Science

Volumn 61, Issue 2, 2016, Pages 405-426

  Kohli, Divyani ^a   Sliuzas, Richard ^a   Stein, Alfred ^a  

^a UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

image classification; informal settlements; object oriented image analysis (OOA); ontology; Pune, India; remote sensing; Slums; spatial metrics; texture

Indexed keywords

DETECTION METHOD; IMAGE ANALYSIS; IMAGE CLASSIFICATION; INFORMAL SETTLEMENT; REMOTE SENSING; SATELLITE IMAGERY; URBAN AREA;

INDIA; MAHARASHTRA; PUNE;

EID: 84965038721     PISSN: 14498596     EISSN: None     Source Type: Journal    
DOI: 10.1080/14498596.2016.1138247     Document Type: Review

References (47)

1. Aguilar, M., Saldaña, M., and Aguilar, F., 2013. GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments. International Journal of Remote Sensing, 34 (7), 2583–2606.10.1080/01431161.2012.747018
2. Albrecht, F., Lang, S., and Hölbling, D., 2010. Spatial accuracy assessment of object boundaries for object-based image analysis. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38 (4), C7.
3. Baatz, M., and Schäpe, A., 2000. Multiresolution segmentation:an optimization approach for high quality multi-scale image segmentation. In: J., Strobl, ed. Angewandte Geographische Informationsverarbeitung XII. Beiträge zum AGIT-Symposium. Heidelberg:Herbert Wichmann Verlag, 12–23.
4. Baltsavias, E.P., 2004. Object extraction and revision by image analysis using existing geodata and knowledge:current status and steps towards operational systems. ISPRS Journal of Photogrammetry and Remote Sensing, 58 (3–4), 129–151.10.1016/j.isprsjprs.2003.09.002
5. Belgiu, M., and Drǎgut, L., 2014. Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 96, 67–75.10.1016/j.isprsjprs.2014.07.002
6. Belgiu, M., Drǎguţ, L., and Strobl, J., 2014a. Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 205–215.10.1016/j.isprsjprs.2013.11.007
7. Belgiu, M., Hofer, B., and Hofmann, P., 2014b. Coupling formalized knowledge bases with object-based image analysis. Remote Sensing Letters, 5 (6), 530–538.10.1080/2150704X.2014.930563
8. Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., and Heynen, 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), 239–258.10.1016/j.isprsjprs.2003.10.002
9. Cleve, C., Kelly, M., Kearns, F.R., and Moritz, M., 2008. Classification of the wildland-urban interface:A comparison of pixel- and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems, 32 (4), 317–326.10.1016/j.compenvurbsys.2007.10.001
10. COI, 2001. Census of India [online]. Office of the Registrar General and Census Commissioner, Government of India. Available from:http://www.censusindia.gov.in/ [Accessed 24 June 2011].
11. Congalton, R., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37 (1), 35–46.10.1016/0034-4257(91)90048-B
12. Drǎguţ, L., Tiede, D., and Levick, S.R., 2010. ESP:a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24 (6), 859–871.
13. Ebert, A., Kerle, N., and Stein, A., 2009. Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and spaceborne imagery and GIS data. Natural Hazards, 48 (2), 275–294.10.1007/s11069-008-9264-0
14. Haala, N., and Brenner, C., 1999. Extraction of buildings and trees in urban environments. ISPRS Journal of Photogrammetry and Remote Sensing, 54 (2–3), 130–137.10.1016/S0924-2716(99)00010-6
15. Herold, M., Scepan, J., and 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 (8), 1443–1458.10.1068/a3496
16. Herold, M., Liu, X., and Clarke, K.C., 2003. Spatial Metrics and Image Texture for Mapping Urban Land Use. Photogrammetric Engineering & Remote Sensing, 69 (9), 991–1001.
17. Herold, M., Mayaux, P., Woodcock, C.E., Baccini, A., and Schmullius, C., 2008. Some challenges in global land cover mapping:An assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112 (5), 2538–2556.10.1016/j.rse.2007.11.013
18. Hofmann, P., 2001. Detecting informal settlements from Ikonos image data using methods of object oriented image analysis-an example from Cape Town (South Africa) In: Jürgens, C., ed. Remote sensing of urban areas. 41–42.
19. Hofmann, P., Strobl, J., Blaschke, T., and Kux, H., 2008. Detecting informal settlements from Quickbird data in Rio De Janeiro using an object based approach. In:T., Blaschke, S., Lang and G.J., Hay, eds. Object-Based Image Analysis. Berlin:Springer, 531–553.10.1007/978-3-540-77058-9
20. Hofmann, P., Blaschke, T., and Strobl, J., 2011. Quantifying the robustness of fuzzy rule sets in object-based image analysis. International Journal of Remote Sensing, 32 (22), 7359–7381.10.1080/01431161.2010.523727
21. Hu, X., and Weng, Q., 2010. Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method. Geocarto International, 26 (1), 3–20.
22. Kit, O., Lüdeke, M., and Reckien, D., 2012. Texture-based identification of urban slums in Hyderabad, India using remote sensing data. Applied Geography, 32 (2), 660–667.10.1016/j.apgeog.2011.07.016
23. Kohli, D., Sliuzas, R., Kerle, N., and Stein, A., 2012. An ontology of slums for image-based classification. Computers, Environment and Urban Systems, 36 (2), 154–163.10.1016/j.compenvurbsys.2011.11.001
24. Kohli, D., Warwadekar, P., Kerle, N., Sliuzas, R., and Stein, A., 2013. Transferability of Object-Oriented Image Analysis Methods for Slum Identification. Remote Sensing, 5 (9), 4209–4228.10.3390/rs5094209
25. Kuffer, M., Barros, J., and Sliuzas, R.V., 2014. The development of a morphological unplanned settlement index using very-high-resolution (VHR) imagery. Computers, Environment and Urban Systems, 48, 138–152.10.1016/j.compenvurbsys.2014.07.012
26. MASHAL, 2011. Pune city slum atlas. Pune:Maharashtra Social Housing and Action League.
27. Myint, S.W., Galletti, C.S., Kaplan, S., and Kim, W.K., 2013. Object vs. pixel:a systematic evaluation in urban environments. Geocarto International, 28 (7), 657–678.10.1080/10106049.2013.776642
28. Neuwirth, R., 2005. Shadow cities:a billion squatters, a new urban world. New York, NY:Routledge.
29. Novack, T., and Kux, H.J.H., 2010. Urban land cover and land use classification of an informal settlement area using the open-source knowledge-based system InterIMAGE. Journal of Spatial Science, 55 (1), 23–41.10.1080/14498596.2010.487640
30. Patino, J.E., and Duque, J.C., 2013. A review of regional science applications of satellite remote sensing in urban settings. Computers, Environment and Urban Systems, 37, 1–17.10.1016/j.compenvurbsys.2012.06.003
31. PMC, 2012. Integrated ward level disaster management plan. Pune:Pune Municipal corporation.
32. Pontius, R.G., and Millones, M., 2011. Death to Kappa:birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32 (15), 4407–4429.10.1080/01431161.2011.552923
33. Ridd, M.K., 1995. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing:comparative anatomy for cities. International Journal of Remote Sensing, 16 (12), 2165–2185.10.1080/01431169508954549
34. Scepan, J., 1999. Thematic validation of high-resolution Global Land-Cover Data Sets. Photogrammetric engineering and remote sensing, 65 (9), 1051–1060.
35. Shekhar, S., 2010. Applying GIS and RS for modeling the growth of slums in Pune City, Maharashtra, India. Thesis (M.Sc.). University of Salzburg.
36. Shekhar, S., 2012. Detecting slums from QuickBird data in Pune using an object-oriented approach. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39 (8), 519–524.
37. Sliuzas, R., Mboup, G., and Sherbinin, A., 2008. Expert group meeting on slum identification and mapping. Enschede, The Netherlands:ITC.
38. Stoler, J., Daniels, D., Weeks, J.R., Stow, D.A., Coulter, L.L., and Finch, B.K., 2012. Assessing the utility of satellite imagery with differing spatial resolutions for deriving proxy measures of slum presence in Accra, Ghana. GIScience & remote sensing, 49 (1), 31–52.
39. Stow, D., Lopez, A., Lippitt, C., Hinton, S., and Weeks, J., 2007. Object-based classification of residential land use within Accra, Ghana based on QuickBird satellite data. International Journal of Remote Sensing, 28 (22), 5167–5173.10.1080/01431160701604703
40. Stow, D.A., Lippitt, C.D., and Weeks, J.R., 2010. Geographic object-based delineation of neighborhoods of Accra, Ghana using QuickBird satellite imagery. Photogrammetric Engineering & Remote Sensing, 76 (8), 907–914.10.14358/PERS.76.8.907
41. Taubenböck, H., and Kraff, N.J., 2014. The physical face of slums:a structural comparison of slums in Mumbai, India, based on remotely sensed data. Journal of Housing and the Built Environment, 29 (1), 15–38.10.1007/s10901-013-9333-x
42. Trimble, 2014. Reference Book. eCognition Developer 9.0. München, Germany.
43. UN-HABITAT, 2006. Analytical perspective of pro-poor slum upgrading frameworks. Nairobi, Kenya:United Nations Human Settlements Programme.
44. UN-HABITAT, 2010. State of World’s Cities 2010/2011. UK and USA:United Nations Human Settlements Programme.
45. UN-HABITAT, 2014. Streets as tools for urban transformation in slums:A street-led approach to citywide slum upgrading. Nairobi, Kenya:United Nations Human Settlements Programme.
46. Van Coillie, F.M.B., Gardin, S., Anseel, F., Duyck, W., Verbeke, L.P.C., and De Wulf, R.R., 2014. Variability of operator performance in remote-sensing image interpretation:the importance of human and external factors. International Journal of Remote Sensing, 35 (2), 754–778.10.1080/01431161.2013.873152
47. Weeks, J., Hill, A., Stow, D., Getis, A., and Fugate, D., 2007. Can we spot a neighborhood from the air? Defining neighborhood structure in Accra, Ghana. GeoJournal, 69 (1-2), 9–22.10.1007/s10708-007-9098-4