Low-altitude images acquired by unpiloted aerial vehicles have the advantages of high overlap, multiple viewing angles and very high ground resolution. These kinds of images can be used in various applications that need high accuracy or fine texture. A novel approach is proposed for parallel processing of low-altitude images acquired by unpiloted aerial vehicles, which can automatically fly according to predefined flight routes under the control of an autopilot system. The general overlap and relative rotation angles between two adjacent images are estimated by overall matching with an improved scale-invariant feature transform (SIFT) operator. Precise conjugate points and relative orientation parameters are determined by a pyramid-based least squares image matching strategy and the relative orientation process. Bundle adjustment is performed with automatically matched conjugate points and interactively measured ground control points. After this aerial triangulation process the high-resolution images can be used to advantage in obtaining precise spatial information products such as digital surface models, digital orthophotomaps and 3D city models. A parallel processing strategy is introduced in this paper to improve the computational time of the photogrammetric process. Experimental results show that the proposed approaches are effective for processing low-altitude images, and have high potential for the acquisition of spatial information at large mapping scales, with rapid response and precise modelling in three dimensions.
