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

describe the different types of settlements is insufficient due to variation in the structure, material, shape and so on. Hence more refined methods such as object oriented approaches are necessary to detect the informal settlements from very high spatial resolution data. With the wide availability of VHR images, automatic object delineation techniques are being extensively researched and have proven to be accurate in urban applications (Ebert, Kerle, & Stein, 2009; Sliuzas, Kerle, & Kuffer, 2008). Recent developments in *object-oriented" image classification (based on image segmentation) have taken advantage of the detailed spatial characteristics of high-resolution datasets. The research in this area has emphasized the reduction of spectral variability within the objects and the incorporation of additional information from spatial and contextual image/ object characteristics (Johnsson, 1994; Blaschke and Strobl, 2001). OOA is capable of using multiple data types during analysis to create meaningful segments. Segmentation 1s an important step preceding the classification of image objects. The classification process can include a variety of information, ranging from spectral mean values for each object, to measures of texture, context and shape. OOA offers great potential because of its ability to include spatial, spectral and contextual characteristics similar to human cognitive image interpretation (Hofmann, 2001; Herold et al, 2002; Van Der Sande et al., 2003; Benz et al., 2004). Even though several studies showed the feasibility of detecting slums by using OOA, their relatively high inner-structural heterogeneity and their varying pattern impede the generation of an automated detection process. In the present paper, the visual interpretation indicators used by experts for slum identification and ground knowledge of slums in the city of Pune, India were used to semi-automate the classification in an OOA environment 2. MATERIALS AND METHODS 2.1 Study area Pune the city (Figurel), selected for present study is one of the fast developing urban agglomerations in Asia. india Rl Maharashtra ^ PuneDiatrier Figure 1.Location of Pune city and Quick Bird scene It ranks eighth at national level and second at state of Maharashtra (Census of India, 2011). It has grown manifolds over the past two decades in terms of population and area. Pune city lies between latitudes 18?25"N and 18?37'N and longitudes between 73°44°E and 73° 57°E and cover an area of 243.96 sq.km. Between 1976 and 1981 the population of Pune city (Table 1) grew by 16.7%, from 1981 to 1991 it grew by 30.2%, and between 1991 and 2001 growth increased to 62.17% (Shekhar., 2004;Shekhar.,2006).The recent census 2011 also showed decade growth rate of 22.6% (Census of India, 2011). India’s Town and Country Planning Organization (TCPO), the technical arm of the ministry of urban development, ranks Pune as a city with third largest number of slums in India. Census year Population 1951 400902 1961 794052 1971 1029466 1981 1202848 1991 1566651 2001 2540069 2011 3115431 Source: Census of India Table 1. Population of Pune city Environment Status Report of Pune city for 2009-10 had stated that at the rate at which the slums in the city were growing, at least 50 per cent of the citys population would be living in slums. Pune slums are congested, have structures made of materials which are considered garbage, such as wood used for packing, plastic sheets, opened out metal tins, galvanised iron sheets, bamboo sheets, etc. and often lack the most basic of facilities for all its inhabitants. Pune's slum population is scattered across the whole city. 2.2 Data base As data source Quick bird scene (60 cm spatial resolution) was used showing parts of Pune city (Figure 1).The image shows the central part of Pune city comprising typical urban features including slums and other formal areas. Cloud free Pan sharpened data with three bands (RGB) of the year 2006 was used for detecting slums from non-slum areas. GIS layers of road and water bodies were also used as thematic layers in the object oriented analysis. Census data and Environmental Status reports of Pune city Municipal Corporation were used as secondary data for this study. 2.3 Methodology The first step towards identifying the slums in OOA is to generate segments, i.e. an automatic division of an image into coherent groups of pixels (segments, objects) and the criteria used to segment an image is a degree of homogeneity within each particular object and heterogeneity among neighbouring objects (Baatz. M., A.Schape, 2000). It was done by using Multi resolution segmentation with the objective choice by using Estimating Scale Parameter (ESP) tool (Drágut et al., 2010). These outputs are called *object primitives', which lead to meaningful ‘objects of interest’ by further refinement (e Cognition, 2010). The segmentation used all image layers as well a meanir level, and en: in an enhanc As th classifi unders identifi Develo process extract classifi class descrip conditi assigne propert Shado Figure 3.1 Se In orde units, t comple means: the mo criteria split in down): basic « method pixels contras comple clusters heterog approac

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