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In this paper a procedure of building a digital terrain model (DTM) from the satellite images is researched. The procedure is based on the authors' previously developed algorithms of fast image matching for building disparity maps implemented on GPUs (Graphics Processing Units). In this paper we propose a computational procedure for constructing a DTM from the satellite stereo images. Experimental studies have shown that while this procedure constructs a DTM that may be less accurate than the one achieved with the use of the ENVI software, it offers a significantly shorter time of processing.

We report on the parallel implementation of a multi-view image segmentation algorithm via segmenting the corresponding three-dimensional scene. The algorithm includes the reconstruction of a three-dimensional scene model in the form of a point cloud, and the segmentation of the resulting point cloud in three-dimensional space using the Hough space. The developed parallel algorithm was implemented on graphics processing units using CUDA technology. Experiments were performed to evaluate the speedup and efficiency of the proposed algorithm. The developed parallel program was tested on modelled scenes.

A trajectory building based on a camera data is one of the most popular tasks in the field of machine vision. In particular, this task appears when it is necessary to navigate in the absence of signals from global navigation systems such as GLONASS and GPS. In this work, study of existing methods of visual odometry for the flight trajectory restoration by shooting an infrared camera of the thermal range were conducted. To improve the accuracy, it is proposed to use the data from inertial sensors. As a result, it is shown that the proposed solution allows to successfully solve the problem of trajectory reconstruction.