Kinga Karwowska received the M.Sc. degree in geoinformatics in 2020 from the Military University of Technology, Warsaw, Poland, where she is currently working toward the Ph.D. degree with the Doctoral School.Damian Wierzbicki received the Ph.D. degree in photogrammetry and remote sensing from the Mil-
Dynamic technological progress has contributed to the development of systems imaging of the Earth’s surface as well as data mining methods. One such example is super-resolution (SR) techniques that allow for the improvement of the spatial resolution of satellite imagery on the basis of a low-resolution image (LR) and an algorithm using deep neural networks. The limitation of these solutions is the input size parameter, which defines the image size that is adopted by a given neural network. Unfortunately, the value of this parameter is often much smaller than the size of the images obtained by Earth Observation satellites. In this article, we presented a new methodology for improving the resolution of an entire satellite image, using a window function. In addition, we conducted research to improve the resolution of satellite images acquired with the World View 2 satellite using the ESRGAN network, we determined the number of buffer pixels that will make it possible to obtain the best image quality. The best reconstruction of the entire satellite imagery using generative neural networks was obtained using a Triangular window (for 10% coverage). The Hann-Poisson window worked best when more overlap between images was used.
Abstract. In recent years, we have been dealing with the dynamic technological progress of the space sector, which allows for the observation of the Earth with better temporal, spatial and spectral resolution. The increasing availability of satellite data has contributed to the development of data processing algorithms. Thanks to the use of digital image processing methods and deep neural networks, it is possible to perform automatic image classification, segmentation or detection and recognition of objects on the images. This article presents the methodology that allows to accelerate the classification process of satellite images representing the Amazon rainforest based on the Transfer Learning method. Additionally, the influence of the choice of optimization, i.e. the network weight estimation strategy, on the classification of objects was checked. In order to verify the method, an additional raster image classifier was created on the basis of Lidar data. Research shows that the transfer learning method allows the preparation of an image classifier based on a small database (less than 100 images representing one class). The network training process can be shortened to a few minutes.
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