Detecting informal settlements from high resolution imagery using an object-based image approach

Ballim, Khaleed (2016-12)

Thesis (MA)--Stellenbosch University, 2016


ENGLISH ABSTRACT: The aim of this study was twofold: evaluate different approaches to deriving normalised digital surface models (nDSM), and develop a robust and transferable methodology for mapping informal dwellings. In the first component, three approaches to extract nDSMs were investigated: (i) light detection and ranging (LiDAR) data, (ii) high resolution aerial photographs in a process of image matching, and (iii) a series of aerial images captured using a hand-held camera using structure from motion (SfM) techniques. SfM is a relatively new technique that has not been widely used for nDSM extraction. This study represented a first attempt at evaluating the three approaches, particularly for mapping informal dwellings. The accuracy of the respective nDSMs was evaluated using vertical profiles, area-based, as well as positional-based accuracy assessment metrics. This provided a clear indication of the robustness of each of the models. Results showed that an nDSM can be successfully extracted in an informal settlement for informal dwelling mapping. Overall LiDAR achieved the highest accuracy in all three accuracy assessments, showing its ability to handle the undefined and complex morphology of informal settlements. To further test the robustness of the nDSMs, each model was applied to an independent test site with varying dwelling density and achieved improved accuracies. In the second component, the utility of high resolution WorldView-2 imagery and object-based image analysis (OBIA) techniques to develop a robust and transferable methodology for mapping individual informal dwellings in the City of Cape Town was tested. A systematic approach was used to objectively identify segmentation and classification parameters. The supervised segmentation parameter tuner (SPT) tool was used to derive optimal segmentation parameters, and was evaluated using an area-based accuracy assessment which resulted in high compactness (> 86%) and correctness (>88%). To reduce data dimensionality and optimize the classification process, the RF algorithm reduced the original WV-2 feature set (n=40) and aerial imagery (n=60) feature sets by 23% and 53%, whereas the CART algorithm reduced the same feature set by 95% and 91% respectively. For classification, a supervised approach was adopted using the random forest (RF) algorithm, and a rule-based classification using a rule set in eCognition software. Although different feature subsets were selected by the RF and CART algorithm for the WV-2 and aerial imagery, similar classification accuracies were achieved in all the test sites.

AFRIKAANS OPSOMMING: Die doel van hierdie studie was tweeledig: om die verskillende benaderings tot die skepping van genormaliseerde digitale oppervlak modelle (nDOM) te evalueer en om ʼn robuuste en oordraagbare metodologie te ontwikkel om informele nedersettings te karteer. In die eerste komponent is drie benaderings tot die onttrekking van ʼn nDOM ondersoek: (i) “light detection and ranging” (LiDAR) data, (ii) hoë resolusie lugfoto’s deur gebruik te maak van ʼn beeldbypassingsproses en (iii) ʼn reeks lugfoto’s met ʼn handgreep kamera geneem van “structure from motion” (SfM) tegnieke gebruik te maak. “Structure from motion” is ‘n nuwe tegniek wat nog nie oor die algemeen gebruik word om nDOM te verkry nie. Hierdie studie is ʼn eerste poging om die drie benaderings te evalueer, met die spesifieke doel om informele nedersettings te karteer. Die akkuraatheid van die onderskeie nDOM is geëvalueer van vertikale profiele, area-gebaseerde sowel as posisioneel-gebaseerde akkuraatheidsassessering statistieke. Dit het die robuustheid van elk van die drie modelle duidelik uitgewys. Die resultate dat ʼn nDOM van ʼn informele nedersetting met sukses verkry kan word en kan sodoende hierdie gebiede karteer. LiDAR het algeheel die hoogste akkuraatheid behaal tydens al drie akkuraatheidsevaluasies, wat hierdie metode se vermoë om die ongedefinieerde en komplekse morfologie van informele nedersettings te hanteer uit wys. Elke model is op ʼn onafhanklike toetsgebied met wisselende woningsdigthede toegepas om die robuustheid van elke nDOM verder uit te lig. In die tweede komponent van die studie word die bruikbaarheid van hoë-resolusie WorldView-2 beelde en objek-gebaseerde beeld analise (OBIA) tegnieke om ʼn robuuste en oordraagbare metodologie om individuele informele nedersettings in Stad Kaapstad te karteer getoets. ʼn Sistematiese benadering is gebruik om segmentasie en klassifikasie parameters te identifiseer. Die gerigte “segmentation parameter tuner” (SPT) is gebruik om optimalesegmentasie parameters te verkry en die akkuraatheid van die segmentasie is geëvalueer deur gebruik te maak van ʼn area-gebaseerde akkuuraatheidsassessering wat gelei het to hoë kompaktheid (> 86%) en korrektheid (>88%). Om data dimensionaliteit te verminder en die klassifikasie proses te optimeer, is die RF algoritme gebruik om die oorspronklike WorldView-2 voorwerp kenmerkstelle (n=140) en oorspronklike aerial voorwerp kenmerkstelle (n=60) ekwivalent aan ʼn dimensionaliteitsvermindering van onderskeidelik 23% en 53%, terywl die CART algoritme ‘n dimensionaliteitsvermindering van onderskeidelik 95% end 91%. ʼn Gerigte benadering tot klassifikasie is gevolg deur gebruik te maak van die “random forest” (RF) algoritme, asook ʼn reël-gebaseerde klassifikasie in eCognition sagteware. Om die robuustheid en oordraagbaarheid van die modelle te assesseer, is elke model op twee onafhanklike gebiede getoets.

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