The paper proposes a semantic segmentation algorithm based on Convolutional Neural Networks (CNN) related to the problem of presenting multispectral sensor-derived images in Enhanced Vision Systems (EVS). The CNN architecture based on residual SqueezeNet with deconvolutional layers is presented. To create an in-domain training dataset for CNN, a semi-automatic scenario with the use of photogrammetric technique is described. Experimental results are shown for problem-oriented images, obtained by TV and IR sensors of the EVS prototype in a set of flight experiments.
In this paper, we propose a background stabilization method for an arbitrary camera movement. We investigate the state of the art algorithms for feature point detection and introduce a composite LBP descriptor to describe the feature points both with an algorithm for feature points matching on a sequence of images. In addition, an algorithm for constructing an affine transformation of the old frame in the sequence into the new one for the tasks of stabilization and image stitching was proposed.