XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/8: Land]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: DETECTING SLUMS FROM QUICK BIRD DATA IN PUNE USING AN OBJECT ORIENTED APPROACH Sulochana Shekhar

Document type:: Multivolume work

Structure type:: Chapter

as were identified tour lines. Further > finding using the E., Valenti, S. and multi-factor spatial existence of illegal vhical Information | landfill Site 2007 tawa, Canada. itellite images and landfill. Ryerson 2010. Selection of g GIS and multi- g and Assessment, dar remote sensing infield University, lated health effects landfill. American 184—190. od for the remote s: formulation and Sensing, 29(4), pp. d Haack. B. 2010. azardous water: a | Yan, W.Y. 2010. ques in monitoring satellite data. A/- op. 542-551. 011. Public health ed near municipal ical Practice and S blic Authority of rmation they have al thanks also are epean, and Dillon rmation that they research work is Discovery Grant. DETECTING SLUMS FROM QUICK BIRD DATA IN PUNE USING AN OBJECT ORIENTED APPROACH Sulochana Shekhar Central University of Karnataka, Gulbarga, India WG, Theme or Special Session: VIII/8: Land KEY WORDS: Slums, Quick bird data, Object oriented Analysis, eCognition, Pune ABSTRACT: We have been witnessing a gradual and steady transformation from a pre dominantly rural society to an urban society in India and by 2030, it will have more people living in urban than rural areas. Slums formed an integral part of Indian urbanisation as most of the Indian cities lack in basic needs of an acceptable life. Many efforts are being taken to improve their conditions. To carry out slum renewal programs and monitor its implementation, slum settlements should be recorded to obtain an adequate spatial data base. This can be only achieved through the analysis of remote sensing data with very high spatial resolution. Regarding the occurrences of settlement areas in the remote sensing data pixel-based approach on a high resolution image is unable to represent the heterogeneity of complex urban environments. Hence there is a need for sophisticated method and data for slum analysis. An attempt has been made to detect and discriminate the slums of Pune city by describing typical characteristics of these settlements, by using eCognition software from quick bird data on the basis of object oriented approach. Based on multi resolution segmentation, initial objects were created and further depend on texture, geometry and contextual characteristics of the image objects, they were classified into slums and non-slums. The developed rule base allowed the description of knowledge about phenomena clearly and easily using fuzzy membership functions and the described knowledge stored in the classification rule base led to the best classification with more than 80% accuracy. 1. INTRODUCTION 1.1 Urbanisation and Slums Today, half the world's population lives in urban areas and by the middle of this century all regions will be predominantly urban, and according to current projections, virtually the whole of the world's population growth over the next 30 years will be concentrated in urban areas (UN-HABITAT, 2010). Above all, this rapid urban growth has been strongly associated with poverty and slum growth. It is felt that slums represent the worst of urban poverty and inequality. The increasing concentration of the urban population in slum areas is generally equated with increasing urban poverty a process recognized as the urbanization of poverty. According to new estimates presented in UN-HABITAT’s report, between the year 2000 and 2010 over 200 million people in the developing world will have been lifted out of slum conditions. But in the course of the same years the number of slum dwellers will be increased by six million every year. Based on these trends it is expected that the world’s slum population will continue to grow if no corrective action is taken in the coming years (UN- HABITAT, 2010). Defining slum raises several conceptual issues, making it difficult to precisely estimate the slum population living in urban areas. Concepts and definitions of slums vary from country to country and even in the same country, slum settlements may be known by different names (Kohli.D, 2011) In order to carry out the urban planning and development tasks necessary to improve living conditions for the poorest world- wide an adequate spatial data basis is needed (Mason, O.S and Fraser, C.S., 1998) and this can only be obtained through the analysis of remote sensing data (Hofmann,P.,2001). Since traditional methods demand more labour, money and time, alternative methods that include sophisticated techniques to extract the information from remote sensing data of very high resolution (VHR) and thus could reduce subjectivity, time and labour (Naga Jyothi., 2008 et al.) and provide more reliable data are need of the hour. 1.2 Related work on detecting slums Many studies used census and field survey as the basis for studying about slums and formed the database for GIS-based mapping (Joshi Pratima, Sen Srinanda and Hobson Jane, 1998; Sliuzas and Kuffer, 2006). Recently very high resolution remote sensing based methods for mapping slums are getting popular among the scientific community (Mason& Fraser, 1998; Sliuzas, Kerle and Kuffer, 2008; Hofmann .P., 2001; Hofmann. P., 2004 et al. ) but there are only very few studies based on Indian situation (Ujjwal Sur, 2004). In most of the remote sensing based studies visual interpretation of data has played major role in identification of slums (Angeles et al., 2009; Sliuzas R.V 2004.,Sliuzas and Kuffer 2008; Baud, Kuffer, Pfeffer, Sliuzas, and Karuppannan (2010). Visual interpretation performed by interpreters familiar with local conditions provides a flexible and useful approach to slum mapping, though it does have shortcomings for repetitive surveys of very large cities due to difficulties in controlling quality over time and between interpreters. Later pixel based image classification is widely used in slum analysis and it also helped to understand the patterns over time and space (Jain, Sokhi and Sur, 2005; Jain, 2007; Weeks et al., 2007). But pixel-based approach on a high resolution image is unable to represent the heterogeneity of complex urban environments. In most cases by using only pixels’ spectral information to

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