Today more and more people are moving to the cities around the world. This puts a lot of strain on the infrastructure as the cities grow in both width and height. To be able to monitor the ongoing change remote sensing is an effective tool and ways to make it even more effective, better and easier to use are constantly sought after. One way to monitor change detection is object based change detection. The idea has been around since the seventies, but it wasn’t until the early 2000 when it was introduced by Blaschke and Strobl(2001) to the market as a solution to the issues with pixel based analysis that it became popular with remote analysts around the world. KTH-SEG is developed at KTH Geoinformatics. It is developed to segment images in order to preform object based analysis; it can also be used for classification. In this thesis object based change detection over an area of Shanghai is carried out. Two different approaches are used; post-classification analysis as well as creating change detection images. The maps are assessed using the maximum likelihood report in the software Geomatica. The segmentation and classification is done using KTH-SEG, training areas and ground truth data polygons are drawn in ArcGIS and pre-processing and other operations is carried out using Geomatica. KTH-SEG offers a number of changeable settings that allows the segmentation to suit the image at hand.  It is easy to use and produces well defined classification maps that are usable for change detection The results are evaluated in order to estimate the efficiency of object based change detection in urban area and KTH-SEG is appraised as a segmentation and classification tool. The results show that the post-classification approach is superior to the change detection images. Whether the poor result of the change detection images is affected by other parameters than the object based approach can’t be determined. / Idag flyttar fler och fler människor in i städer runt om i världen. Det utgör en stor påverkan på den befintliga infra-strukturen då städerna växer på både höjden och bredden. För att kunna bevaka den förändring som sker så används ofta fjärranalys som ett effektivt verktyg. Sätt att utveckla befintliga tekniken försöker man hela tiden hitta nya, enklare och mer effektiva sätt att bevaka förändring finns alltid på horisonten. Objektbaserad förändrings analys är ett sätt att bevaka förändringar. Iden om att använda objekt baserad analys har funnits sedan 70-talet, men det var först i början av 2000-talet, då Blaschke och Strobl(2001) introducerade tekniken som en lösning på de problem man stöter på i pixelbaserad analys, som tekniken blev populär bland fjärranalytiker världen över. KTH-SEG är ett program utvecklat på KTH Geoinformatik avdelning. KTH-SEG är utvecklat för att segmentera bilder inför objektbaserad analys. Dessutom utför programmet klassificering. I det här arbetet utförs objektbaserad förändrings analys över ett område i Shanghai. För att hitta de förändringar som har skett har två tillvägsgångssätt använts: dels har analys av bilder efter klassificeringen gjorts och dels har bilder som i sig själva skall visa den förändring som har skett skapats, så kallade förändringsbilder. Bildernas pålitlighet är utvärderad genom att använda ”maximum likelihood report” i programmet Geomatica.   Segmentering och klassificering är gjort i programmet KTH-SEG, träningsområden och testområden är skapade i ArcGIS och förbehandling av bilder samt andra operationer är gjorda i Geomatica. KTH-SEG erbjuder många valmöjligheter för att påverka segmenteringsresultatet. Den är enkelt att använda och producerar tydliga klassificerade bilder som är användbara för analys. Resultatet utvärderas för att bestämma hur effektivt det är att använda objektbaserad förändrings analys av urbana områden och KTH-SEG utvärderas som ett segmenterings- och klassifikations verktyg. Resultaten visar att förändringsbilder ger ett sämre resultat än bilder som analyseras efter klassifikationen. Huruvida det dåliga resultatet på förändingsbilderna beror på andra omständigheter än tillvägagångssättet med objekt baserad klassifikation kan inte bestämmas. Mycket tyder dock på att det är valet av två bilder från olika satelliter som ger det dåliga resultatet.

The objective of this licentiate thesis is to develop novel algorithms and improve existing methods for urban land cover mapping and urban extent extraction using multi-temporal remote sensing imagery. Past studies have demonstrated that synthetic aperture radar (SAR) have very good properties for the analysis of urban areas, the synergy of SAR and optical data is advantageous for various applications. The specific objectives of this research are: 1. To develop a novel edge-aware region-growing and -merging algorithm, KTH-SEG, for effective segmentation of SAR and optical data for urban land cover mapping; 2. To evaluate the synergistic effects of multi-temporal ENVISAT ASAR and HJ-1B multi-spectral data for urban land cover mapping; 3. To improve the robustness of an existing method for urban extent extraction by adding effective pre- and post-processing. ENVISAT ASAR data and the Chinese HJ-1B multispectral , as well as TerraSAR-X data were used in this research. For objectives 1 and 2 two main study areas were chosen, Beijing and Shanghai, China. For both sites a number of multitemporal ENVISAT ASAR (30m C-band) scenes with varying image characteristics were selected during the vegetated season of 2009. For Shanghai TerraSAR-X strip-map images at 3m resolution X-band) were acquired for a similar period in 2010 to also evaluate high resolution X-band SAR for urban land cover mapping. Ten  major landcover classes were extracted including high density built-up, low density built-up, bare field, low vegetation, forest, golf course, grass, water, airport runway and major road. For Objective 3, eleven globally distributed study areas where chosen, Berlin, Beijing, Jakarta, Lagos, Lombardia (northern Italy), Mexico City, Mumbai, New York City, Rio de Janeiro, Stockholm and Sydney. For all cities ENVISAT ASAR imagery was acquired and for cities in or close to mountains even SRTM digital elevation data. The methodology of this thesis includes two major components, KTH-SEG and KTH-Pavia Urban Extractor. KTH-SEG is an edge aware region-growing and -merging algorithm that utilizes both the benefit of finding local high frequency changes as well as determining robustly homogeneous areas of a low frequency in local change. The post-segmentation classification is performed using support vector machines. KTH-SEG was evaluated using multitemporal, multi-angle, dual-polarization ASAR data and multispectral HJ-1B data as well as TerraSAR-X data. The KTH-Pavia urban extractor is a processing chain. It includes: Geometrical corrections, contrast enhancement, builtup area extraction using spatial stastistics and GLCM texture features, logical operator based fusion and DEM based mountain masking. For urban land cover classification using multitemporal ENVISAT ASAR data, the results showed that KTH-SEG achieved an overall accuracy of almost 80% (0.77 Kappa ) for the 10 urban land cover classes both Beijign and Shanghai, compared to eCognition results of 75% (0.71 Kappa) In particular the detection of small linear features with respect to the image resolution such as roads in 30m resolved data went well with 83% user accuracy from KTH-SEG versus 57% user accuracy using the segments derived from eCognition. The other urban classes which in particular in SAR imagery are characterized by a high degree of heterogeneity were classified superiorly by KTH-SEG. ECognition in general performed better on vegetation classes such as grass, low vegetation and forest which are usually more homogeneous. It is was also found that the combination of ASAR and HJ-1B optical data was beneficial, increasing the final classification accuracy by at least 10% compared to ASAR or HJ-1B data alone. The results also further confirmed that a higher diversity of SAR type images is more important for the urban classification outcome. However, this is not the case when classifying high resolution TerraSAR-X strip-map imagery. Here the different image characteristics of different look angles, and orbit orientation created more confusion mainly due to the different layover and foreshortening effects on larger buildings. The TerraSAR-X results showed also that accurate urban classification can be achieved using high resolution SAR data alone with almost 84% for  eight classes around the Shanghai international Airport (high and low density built-up were not separated as well as roads and runways). For urban extent extraction, the results demonstrated that built-up areas can be effectively extracted using a single ENVISAT ASAR image in 10 global cities reaching overall accuracies around 85%, compared to 75% of MODIS urban class and 73% GlobCover Urban class. Multitemporal ASAR can improve the urban extraction results by 5-10% in Beijing. Mountain masking applied in Mumbai and Rio de Janeiro increased the accuracy by 3-5%.The research performed in  this thesis has contributed to the remote sensing community by providing algorithms and methods for both extracting urban areas and identifying urban land cover in a more detailed fashion. / <p>QC 20140625</p>