New anthropogenic objects detection in multi-temporal satellite images of the Belarusian space system of Earth remote sensing - Semianenka, Leanid V.; Kochyk, Eugene N.; Saroka, Aliaksandr M. (Journal of the Belarusian State University. Physics, 2025)
The article considers the solution to the problem of detecting new anthropogenic objects during environmental monitoring in the territory of Belarus using multi-temporal panchromatic satellite images of the Belarusian space system of Earth remote sensing and a deep learning neural network that ensures the most reliable detection of changes in ground objects while minimising false positives. To solve this problem, an analysis of deep learning neural network models for detecting new anthropogenic objects based on semantic segmentation of multi-temporal satellite images was carried out. Based on this results, a deep learning neural network model was selected, the hypothesis about the need for training on our own formed dataset of anthropogenic objects was tested, training and validation datasets were formed. The training of the selected deep learning neural network model, including the optimisation of settings and selection of hyperparameters, was carried out to implement the task of detecting new anthropogenic objects based on multi-temporal panchromatic satellite images. The obtained results of this research showcased the practical feasibility of automating the detection of new anthropogenic objects during environmental monitoring.
Keywords: detection; environmental monitoring; multi-temporal satellite images; Belarusian space system of Earth remote sensing; neural network; deep learning; semantic segmentation