Process of object recognition in satellite images of high resolution is a complex task associated with a time consumption and complexity of the operator's work. This paper describes an innovative approach for solving this problem. Based on monochromatic high-resolution satellite images (in the process of using data from the QuickBird satellite with a maximum resolution of 0.6 meters per pixel) geodata bitmap and vectorized output are received (shape files). The principle of object recognition in a satellite image is based on the allocation of edges in the gradient transition using a threshold filter. Obtained data is then transformed to a vector output using straight line detection and connected components analysis. The proposed method allows to process satellite images of large size with high performance. The performance of the proposed method can be improved by using GPU-based computations.