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Algorithms for obtaining, complexing and processing weakly formalized heterogeneous aerial photography data

In the paper requirements are formulated for representation models, algorithms for obtaining, complexing and processing weakly formalized heterogeneous data to build a spatial model of the research object. An approach to aggregation of multispectral data has been developed using the example task of combining visual data from aerial photography and geographic coordinates of objects obtained using unmanned aerial vehicles. An algorithm for combining visual data is proposed based on the recurrent combining of aerial photography images, which includes key point’s detection in the images and building a RANSAC regression model based on these points. An algorithm for comparing geographic coordinates with points of the combined image is also proposed. The algorithm is based on the idea of equivalent transformations over visual data and geographic coordinates of objects. The proposed algorithms are implemented as a software tool, it is tested on several sets of aerial photography data. Prospects for the development of the proposed approach and the shortcomings of its algorithms that need to be eliminated are identified. It has been established that further optimization of memory use when combining aerial photography images and further research in the direction of compensating for perspective distortion are necessary. The applicability of the proposed approach is shown in the problems of obtaining, complexing, processing and visualizing weakly formalized multispectral data in the field of aerial photography of images of various ranges (thermal imaging, optical, etc.), as well as in other areas of data processing and analysis, such as detection and semantic segmentation objects in aerial photography images. Additional spatial information can improve the accuracy of classification and segmentation of objects in images. Read more...