Analysis of information content on hypertemporal UAV images


  • Lóránt Biró Budapest Business University, Faculty of Commerce, Hospitality and Tourism, e-mail: (corresponding author)
  • József Berke Dennis Gabor University, Drone Technology and Image Processing Scientific Lab, e-mail:
  • Kristóf Kozma-Bognár Hungarian University of Agricultural and Life Sciences, Festetics György Doctoral School, e-mail:
  • Veronika Kozma-Bognár Dennis Gabor University, Drone Technology and Image Processing Scientific Lab, e-mail:


UAV, hypertemporal, multispectral, plant protection, classification


Today, the data provided by drones extremely useful information for professionals. The processing of large data sets collected by UAVs, on the other hand, may require different methodological elements based on the properties of the sensors placed in each camera system. The sensors placed on the carrier devices can significantly influence not only the collection of data, but also the evaluations appropriate for the given purpose. The data sets created by the sensors can be characterized by different geometric, spectral and temporal resolutions for each camera system. We can characterize the information content of the spectral layers of the Bayer-type CFA filter (Color Filter Array) and Global Shutter sensors by calculating information-theoretic entropy. If we have different spectral, geometric, and temporal data series available after the recording, the processing can be done by processing the data series separately or together. In the case of aerial photographs with different characteristics, data fusion procedures can also be used in the data processing process, which poses many challenges for remote sensing specialists. Properly performed data fusion can further increase the potential of the data. In our article, we present the information content-based processing of our environmental protection aerial surveys carried out in the sample area of Kis-Balaton. During image processing, we performed geodesic-based and pattern-matching-based integration of the data, the results of which are also presented with an entropy-based analysis of the images. We extended our investigations to the most frequently used image classification procedures in practice, and we also present the analysis of the error matrices related to the analysis of the result images of the procedures and the obtained Kappa indices. All of these were done in the manner described above because they do not require unique solutions and farmers, or users can do them even with basic knowledge.


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