Neural network model for detection of changes in forest environment using multispectral images

Abstract

The article presents the task of detecting changes in the forest environment using multispectral images for balanced forest management and monitoring of the regional forest ecosystem. The method of semantic segmentation based on assigning each pixel a corresponding class label is used for recognition, object classification on the image, and analysis of multispectral images. By studying the spectral characteristics of pixels, the neural network automatically extracts and memorizes features from the data, using them for classification and change detection. Experimental results based on the developed software prototype confirm the reliability of the theoretical aspects of the model for change detection in the forest environment. Moreover, the neural network model for detecting changes in the forest environment using multispectral images has practical significance and can be applied to solve real tasks of the regional forest ecosystem of the Republic of Buryatia.