Метод гауссова обучения нейронной сети для решения задачи распознавания многоспектральных спутниковых изображений

Abstract

The methods of Gaussian computer vision and machine learning used to process multispectral satellite images in the detection and recognition of research objects --- banana plantations in the area of El Porvenir on the coast of Ecuador --- are considered. Using computer vision and machine learning techniques, processing of multispectral satellite images and indirect raster data after calculating NDVI and NDWI vegetation indices, classification of vegetation and water objects was performed. The analysis of multispectral satellite image data is complicated by the presence of clouds and rasters that do not contain data other than the object of study. The machine learning method when labeling data with validation by vegetation indices is used in segmentation of classes that do not participate in the computer vision analysis in the detection and recognition of the research object. The probability of the given positive experimental results is ~ 96 %. The proposed methods of objects detection and recognition on multispectral satellite images can be implemented in agricultural systems to improve, analyze and assess the needs of crops, which are part of agriculture as the primary sector of the economy, to meet the needs of food and raw materials production. Thus, with the increased demand for agricultural products, it is necessary to introduce technologies that guarantee the quality and high productivity of agricultural machinery