International Journal of Computer Applications (0975 – 8887) Volume 145 – No.3, July 2016 25 Automatic Building Detection from Satellite Images using Internal Gray Variance and Digital Surface Model Amit Raikar P.G Student P.D.A College of Engineering Kalaburagi, Karnataka, India Geeta Hanji Associate Professor P.D.A College of Engineering Kalaburagi, Karnataka, India ABSTRACT Automatic building extraction is considered recently as an active research in remote sensing operation. It has been going on for more than 20 years but the automated extractions still encounter problems due to image resolution, variation and level of details. Because of high-object density and scene complexity this is going to be an even greater challenge especially in urban areas. This paper is going to present an ideal framework for high-resolution panchromatic images which helps in reliable and accurate building extraction operation. Proposed framework along with the consideration of domain knowledge (spatial and spectral characteristics) provides features like the nature of objects in the scene, their optical interactions and their impact on the resulting image. To analyze geometric nature of scene in better way we are using Digital Surface Model (DSM). Proposed algorithm has been evaluated using a variety of images from IKONOS and QuickBird satellites. The results demonstrate that the proposed algorithm is accurate and efficient in comparison with the state of art methods. General Terms Building detection, clustering, enhancement, feature extraction, high resolution, morphology, remote sensing, automatic detection, segmentation, and thinning. Keywords Digital Surface Model, DSM, Internal Gray Variance, and IGV. 1. INTRODUCTION Satellite and aerial images are playing major role in acquiring information about objects on the Earth's surface. For many applications the main attention is to identify the objects and targets within the aerial images. Some example are rescue operations and defense applications. Since from the past human used to analyze the aerial image to recognize the building objects, and human understanding of these objects has become expensive and tends to be impractical because of quality of data and increased applications. In the study of aerial images, the identification of buildings and other man- made structures has become a common topic. Other applications such as creating maps or databases for geographic information systems, urban planning are also more demanding. The potential of identifying the building automatically and efficiently helps to understand the scene collected from the image contents and going to be used in database application like content-based retrieval. Further applications to be considered are planning of residential development, evaluation of damage and detection of military target. Because of basic geometrical nature of building it has attracted most of the applications, which in turn going to decrease the effect of inter-building occlusion in aerial images. The building detection in aerial image is considered to be a tough task because along with the building there are huge numbers of other objects like vegetation, water bodies, and roads. The potential for similarity of imaged roofs to a background is also another issue to be considered. The main objective here is to identifying the structure of object of interest and segmenting it from the background so that it can be represented for later understanding. The primary operation to be considered here is identification and isolation which are difficult operations to perform, because of the presence of natural texture of vegetation, area occupied by water and other different kinds of elements which are generally present on or near the object of interest, specifically in the aerial image. Since from late 1980s, the identification of man-made structure and buildings has considered as the active field of interest. One of the standard method considered for solving the problem of identification is assuming that an object (or building) has four edges. Making this assumption, other solutions depend on parameters such as edge, line and corner detection. These parameters are used by grouping and achieving shape detection of objects like rectangles and parallelograms. Later in the second method, DSM module is used. It also helps in obtaining the height of the building from the first ground. DSM helps in extracting buildings, if there is no shadow present in the scene for the relevant building object. The combination of both IGV and DSM leads to detection of buildings. 2. LITERATURE SURVEY Previously standard approaches considered as the probability model, in this approach spatial context parameters are able to increase the accuracy value of classification process. Additionally other sources of data have also been considered, such as range data or stereo vision by making use of more than one image of the scene for identification, with gray-scale image. Specifically considering the single image data, the information like height parameter is not available; this is the why multiple images were considered to deduce the height information. This height information is combined with spatial information in order to get better performance of detection techniques. In one of the method [17], the parameters like corner and edge values collected from specific families of polygon objects are generally going to be compared with corner and edge values that are specifically collected from images. Others have also achieved to collect and analyze image contents for building extraction by making use of morphological methods; the approaches make use of morphological fitter values for identification of building. Few to mention are white top-hat, black top-hat or other geometrical filters. One of the technique considers shadow as case of elevated man-made structures; although this technique needs details like direction of the sun within the specific image, its angular elevation. Large numbers of methods for