Construction of Three-Dimensional Models of Rigid Objects from Satellite Imagesof High Spatial Resolution Using Convolutional Neural Networks
A method for constructing three-dimensional models of rigid objects from one satellite image is described, based on the use of two convolutional neural networks that sequentially process high-resolution satellite images. The first neural network performs integral image analysis for the purpose of segmentation and selection of objects of specified physical classes. The second neural network performs local image analysis and works with images segmented by the first neural network in areas in the image, presumably containing objects of specified classes. An algorithm for reconstructing a three-dimensional model of an object from raster areas of a segmented image obtained from the results of local analysis is described, based on regression analysis, an assessment of equivalent figures, as well as linearization and polarization of contours. The results of the operation of the algorithm are given on the example of railway infrastructure facilities. The results of the construction of three-dimensional models of three objects of the railway infrastructure, revealed by the results of the operation of neural networks for four informative classes of areas, such as roofs, walls, rails, support pillars, are shown. Standard dimensions such as rail gauge (1.52 m) and pole height (11.35 m) are used to estimate scaling factors to provide estimates of base dimensions and object heights. The possibility of building 3D models of objects of various areas from 210 to 4200 m2 is shown.