Morphological slum index for slum extraction from high-resolution remote sensing imagery over urban areas

References

• Aravena Pelizari P, Spröhnle K, Geiß C, Schoepfer E, Plank S, Taubenböck H. 2018. Multi-sensor feature fusion for very high spatial resolution built-up area extraction in temporary settlements. Remote Sens Environ. 209:793–807.
   Web of Science ®Google Scholar
• Baud I, Kuffer M, Pfeffer K, Sliuzas RV, Karuppannan S. 2010. Understanding heterogeneity in metropolitan India: the added value of remote sensing data for analyzing sub-standard residential areas. Int J Appl Earth Obs Geoinf. 12(5):359–374.
   Web of Science ®Google Scholar
• Blaschke T, Hay J, Kelly M, Lang S, Hofmann P, Addink E, Feitosa RQ, Meer F, Werff H, Coillie F, et al. 2014. Geographic object-based image analysis – towards a new paradigm. ISPRS J Photogramm Remote Sens. 87(100):180–191.
   PubMedGoogle Scholar
• Cohen B. 2006. Urbanization in developing countries: current trends, future projections, and key challenges for sustainability. Technol Soc. 28(1-2):63–80.
   Google Scholar
• Congalton RG. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ. 37(1):35–46.
   Web of Science ®Google Scholar
• Duque JC, Patino JE, Betancourt A. 2017. Exploring the potential of machine learning for automatic slum identification from VHR imagery. Remote Sens. 9(9):895.
   Google Scholar
• Ehresmann P. 2004. Nairobi's housing crisis: an analysis of the Kenya Slum upgrading programme. Honors Theses. 409:1963–2015.
   Google Scholar
• Gilbert A. 2007. The return of the slum: does language matter. I J Urban Reg Res. 31(4):697–713.
   Web of Science ®Google Scholar
• GoI 2013. Slum free city plan of Action-Madurai Corporation: Draft Report. Rajiv Awas Yojana (RAY)- Scheme Guidelines: p. 1–152.
   Google Scholar
• Honglei Y, Junhuan P, Bairu X, Dingxuan Z. 2013. Remote sensing classification using fuzzy C- means clustering with spatial constraints based on Markov random field. Eur J Remote Sens. 46(1):305–316.
   Web of Science ®Google Scholar
• Jung S, Lee K, Lee WH. 2022. Object-based high-rise building detection using morphological building index and digital map. Remote Sens. 14(2):330.
   Google Scholar
• Kohli D, Sliuzas R, Stein A. 2016. Urban slum detection using texture and spatial metrics derived from satellite imagery. J Spat Sci. 61(2):405–426.
   Web of Science ®Google Scholar
• Kuffer M, Barrosb J. 2011. Urban morphology of unplanned settlements: the use of spatial metrics in VHR remotely sensed images. Proc Environ Sci. 7:152–157.
   Google Scholar
• Kuffer M, Barros J, Sliuzas R. 2014. The development of a morphological unplanned settlement index using very-high-resolution (VHR) imagery. Comput Environ Urban Syst. 48:138–152.
   Web of Science ®Google Scholar
• Kuffer M, Pfeffer K, Sliuzas R. 2016. Slums from space - 15 years of slum mapping using remote sensing. Remote Sens. 8(6):455.
   Google Scholar
• Kuffer M, Pfeffer K, Sliuzas R, Baud I. 2016. Extraction of slum areas from VHR imagery using GLCM variance. IEEE J Sel Top Appl Earth Observ Remote Sens. 9(5):1–11.
   Web of Science ®Google Scholar
• Lv ZY, Zhang P, Benediktsson JA, Shi WZ. 2014. Morphological profiles based on differently shaped structuring elements for classification of images with very high spatial resolution. IEEE J Sel Top Appl Earth Observ Remote Sens. 7(12):4644–4652.
   Web of Science ®Google Scholar
• Mahabir R, Croitoru A, Crooks AT, Agouris P, Stefanidis A. 2018. A critical review of high and very high resolution remote sensing approaches for detecting and mapping slums: trends, challenges and emerging opportunities. Urban Sci. 2(1):8.
   Google Scholar
• Montgomery MR. 2008. The urban transformation of the developing world. Science. 319(5864):761–764.
   PubMed Web of Science ®Google Scholar
• Owen X, Wong DW. 2013. An approach to differentiate informal settlements using spectral, texture, geomorphology and road accessibility metrics. Appl Geogr. 38:107–118.
   Web of Science ®Google Scholar
• Panek J, Lenka S. 2015. Community mapping in urban informal settlements: examples from Nairobi, Kenya. Electron J Inf Syst Develop Count. 68(1):1–13.
   Web of Science ®Google Scholar
• Prabhu R, Alagu Raja RA. 2018. Urban slum detection approaches from high-resolution satellite data using statistical and spectral based approaches. J Indian Soc Remote Sens. 46(12):2033–2044.
   Web of Science ®Google Scholar
• Prabhu R, Parvathavarthini B, Alagu Raja RA. 2021. Slum extraction from high resolution satellite data using mathematical morphology based approach. Int J Remote Sens. 42(1):172–190.
   Web of Science ®Google Scholar
• Soille P, Vogt P. 2009. Morphological segmentation of binary patterns. Patt Recog Lett. 30(4):456–459.
   Web of Science ®Google Scholar
• Taubenbock H, Kra NJ, Wurm M. 2018. The morphology of the arrival city—a global categorization based on literature surveys and remotely sensed data. Appl Geogr. 92:150–167.
   Web of Science ®Google Scholar
• UN. 2002. World Urbanization Prospects; the 2001 Revision: Data tables and highlights. New York: United Nations Human Settlement Programme; p. 1–190. http://www.megacities.uni-koeln.de/documentation/megacity/statistic/wup2001dh.pdf
   Google Scholar
• UN. 2015. Habitat. Habitat III Issue Papers 22—Informal settlements. United Nations Conference on Housing and Sustainable Urban Development. Oct 17–20. (Quito, Ecuador); p. 150–156. http://habitat3.org/wp-content/uploads/Habitat-III-Issue-Papers-report.pdf.
   Google Scholar
• UN. 2016. Tracking Improvement in the Lives of Slum Dwellers Technical Report; Participatory Slum Upgrading Programme. United Nations Human Settlements Programme; Nairobi (Kenya); p. 1–98.
   Google Scholar
• Vogt P, Ferrari JR, Lookingbill TR, Gardner RH, Riitters GH, Ostapowicz K. 2009. Mapping functional connectivity. Ecol Indic. 9(1):64–71.
   Web of Science ®Google Scholar
• Wang J, Cheng W, Luo W, Zheng X, Zhou C. 2017. An iterative black top hat transform algorithm for the volume estimation of lunar impact craters. Remote Sens. 9(9):952.
   Google Scholar
• Williams TKA, Wei T, Zhu X. 2020. Mapping urban slum settlements using very high-resolution imagery and land boundary data. IEEE J Sel Top Appl Earth Obser Remote Sens. 13(1):166–177.
   Google Scholar
• Wurm M, Taubenböck H. 2018. Detecting social groups from space—assessment of remote sensing-based mapped morphological slums using income data. Remote Sens Lett. 9(1):41–50.
   Web of Science ®Google Scholar
• Wurm M, Taubenbock H, Weigand M, Schmitt A. 2017. Slum mapping in polarimetric SAR data using spatial features. Remote Sens Environ. 194:190–204.
   Web of Science ®Google Scholar