Object based change detection in urban area using KTH-SEG
Abstract: 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.
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