Object-oriented Image Analysis, Urban Remote Sensing
Remote Sensing, in the context of the present exercise, is the acquisition of information about the land surface without a site observation. Satellite imagery is used for this purpose; the sensors are able to detect propagated signals reflected from the earth’s surface in a number of different frequencies including, but not limited to, visible light. For this exercise, we will examine whether remote sensing techniques are applicable in studies of urban areas and to what extend. We will also experiment with object oriented image processing based on segmentation and we will learn how to interpret the results of such operations.
Part 1 refers to the analysis of a QuickBird image covering the northern urban fringe areas of Accra, Ghana. Part 2 assesses the results of the software operations by comparing its results to “ground-truth”, as perceived by the human eye.
Materials and Data
For the first part, the software used for the image analysis was eCognition Developer, which is well suited for analyzing high-resolution satellite images and orthophotos covering complex urban areas. The source data was a 4-band QuickBird satellite image covering an area 4,5 × 5,6 km over the northern urban fringe areas of Accra, Ghana, with spatial resolution of 0,6 m taken on 15 January 2009. For the second part, a smaller image of Accra region was used.
For the first part, the QuickBird image was loaded to eCognition Developer. The first step was to segment the image in two levels: the first level was named “object level” (OL), and the second level was named “neighborhood level” (NL). The NL was less segmented than the OL because effort was made to create larger segments of neighboring objects, which would be unions of parts of the same object (i.e. a house). The best segmentation was selected by trial and error— entering multiple combinations of input values to determine which combination gives the more realistic results. The next step was to classify the segments. Three classes were defined according to the level of urban development: “new development”, “mostly rural” and “fully urban”. For the classification to work, samples had to be assigned to each of the defined classes. This was done by selecting the most representative segments for each case. The classification output can be seen in the following image.
For the segmentation in Part 2, a similar operation took place. In addition to the segmentation, however, a ground-truth base had to be produced. The ground-truth was the manual digitizing of a number of buildings in ArcMap.
After the segmentation had been produced, is was compared to the manual digitizing and the level of accuracy (accuracy assessment) was determined. In particular, the areas of the buildings of interest (the ones which would be used for ground-truth assessment) were calculated and then compared to the areas of the segments referring to each of the buildings (Image 2).
Results and Discussion
The produced segmentation map of Accra in Part 1 indicates a fully urban development (blue color) in the south-western part of the image. A second fully urban center is located slightly northern and slightly western from the main development. The mostly rural parts are located in the western and north-western parts of the image (green color). The purple colored segments are areas under urban development and are located between the “zones” of full development and rural areas, with the exception of the south-eastern part, where they seem to be mixed.
Concerning the second part, it is obvious that the object-based segmentation cannot produce the same accurate results that a manual digitizing can produce. As shown in the images above, there is a spatial difference between the area covered by the structures and the area of their respective segments; however, the extend of the difference is not the same for all types of structures.