АНАЛІЗ ПІДХОДІВ ДО ВИДАЛЕННЯ ХМАРНОСТІ НА КОСМІЧНИХ ЗНІМКАХ ДИСТАНЦІЙНОГО ЗОНДУВАННЯ ЗЕМЛІ

Abstract

The modern development of space technologies and remote sensing creates unique opportunities for solving problems in many areas, including the military. Remote sensing imagery often plays a key role in decision-making at all levels of military command, so one of the most important tasks in this context is cloud detection and extraction. This is an important stage of remote sensing data processing aimed at reconstructing information hidden by clouds. The article is devoted to the analysis of different approaches to cloud removal and improvement of the data quality. The approaches based on the use of various image processing algorithms (traditional approaches) have certain limitations associated with the frequent loss of useful information. Special attention is paid to deep learning methods, which have gained popularity in solving cloud removal problems. Deep Neural Networks show great potential for recovering information on satellite images that is hidden by clouds. This paper discusses various Deep Neural Networks architectures, such as convolutional neural networks, conditional generative adversarial networks, and their modifications. Their advantages and disadvantages are also considered. The use of such methods is more accurate and efficient compared to traditional image processing methods, as neural networks can adapt to various conditions and types of images. The analyzed disadvantages of fusing purely optical data led to the conclusion that the best approach to solving the problem of removing clouds from satellite images would be to combine optical and radar data. Despite the complexity of such an approach, it can show the greatest efficiency in solving the problem considered in this article. The challenges and prospects for further improvement of cloud removal methods on satellite images are considered. In particular, the use of artificial intelligence algorithms for automatic cloud detection and removal, as well as the need to create standardized methods for comparing and evaluating the effectiveness of different approaches. Keywords: satellite imagery; remote sensing; cloud cover; neural networks.