Abstract
Study of the Earth’s surface objects reflectance characteristics with unmanned aerial vehicles is one of the most actual remote sensing trends. Aim of this work was to develop a method for obtaining of photospectral data using unmanned aerial spectrometry vehicle.An adaptation of the cameras spatial resolution evaluating technique based on a specialized target photographic fixation was proposed. A method for synchronizing of the camera and spectrometer of the videospectral device was also proposed. It was based on an experiment with spectra and screen images recording. Different colors were sequentially displayed on the screen. The percentage contribution of each of colors to the “mixed” spectra was calculated. So the out-of-sync time estimation became possible. In addition the work proposed the method for combining images and spectra with their merging into photospectral images. The method allows to consider the aircraft displacement when linking the spectrometer field of view to the RGB image. The way for photospectral images combining based on the images key points detectors was also proposed.Spatial resolutions for 3 aerial vehicle cameras were obtained. The study showed that the spatial resolution decrease of Zenmuse H20T caused by the device carrier movement with a speed of up to 5 m/s can be ignored. The videospectral device camera and spectrometer out-of-sync time was evaluated. An automatic merging of a set of images using key points detection was made. The spectrometry areas were linked to the panoramic image. The reflectance coefficients were obtained for each of the areas in the range of 350–900 nm. The areas to image linking accuracy was 84.9 ± 11.6 %.A discrepancy between the angular spatial resolution values got experimentally and theoretically was revealed as a result of the cameras spatial resolution evaluating. This indicates the importance of the imaging equipment spatial resolution experimental evaluation. The videospectral device spectrometer and observation camera out-of-sync time evaluation made it possible to correct the data recording time. This led to the timing error standard deviation reduction from 142 ms to 15 ms. The way for the unmanned aerial spectrometry vehicle data obtaining in a photospectral representation was developed. The proposed methods and techniques can be used in similar unmanned systems.
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