A comparative study on extraction of buildings from Quickbird-2 satellite imagery with & without fusion

Extraction of building from very high resolution satellite imagery is a challenging task. Many automatic algorithms are proposed to extract buildings from remote sensing imageries, but most of the algorithms detect only rectangular buildings very effectively (i.e. buildings with the same size and shape). In this paper, an attempt is made to extract buildings with different shape, size, color and pattern from Quickbird-2 imagery. In the automatic method, firstly the adaptive k means clustering algorithm is performed to classify the pixels into a number of classes which then is followed by morphological operators to extract the buildings. The manual method is also implemented to extract building feature. Consequently, both, the automatic and manual methods are adopted on the original Multispectral (MS) image and on the fused image obtained by fusing Quickbird-2 Panchromatic (Pan) image with MS image using the Fuze Go method. The performance of both the methods for the extraction of buildings is evaluated using qualitative and metric analysis. The experimental results show that both the methods are performed reasonably well. However, improving the spatial resolution of the original MS image by fusion helps to determine the buildings information more precisely in terms of spatially as well as spectrally