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About One Approach to the Construction of Clustering and Classification Grid-Type Algorithms

Applied problems of studying the earth's surface using satellite images of remote sensing of the Earth are considered for the study of forest, agricultural, water and other natural resources, where clustering and classification algorithms are instrumental research methods. It is noted that the most well-known procedures for classifying and segmenting multispectral space images in GIS systems, such as ArcGIS, ERDAS, ENVI, are built-in. The need to expand the toolkit for a more efficient solution of applied problems of this class is noted. New universal clustering and classification algorithms based on a unified approach are proposed. Both methods belong to grid-type algorithms, and at the first stage of their work they group points of a set of n - dimensional vectors into grid cells, each cell saves only the numbers of points belonging to it and is characterized by a unique code. The vector grid spacing is a parameter of the method and is set by the user using a single integer value. At the next stage, the clustering algorithm combines the cells and the points belonging to them into clusters using the cell neighborhood principle. In this case, the algorithm does not attach the next cell to the cluster in the case when its density is less than the specified value. The classification algorithm refers the points of the cell of the main set to the class to which the cell with the same code of the training set belongs. The algorithms can be used to process large data sets of large spatial dimensions, including satellite images. Clustering and classification algorithms do not require a preliminary specification of the number of clusters and information about the nature of the distribution of points in the input set.

Remote Sensing Methods, Images Segmentation, Clustering Algorithms, Classification Algorithms, Grid Methods, Neighborhood Relation, Cell Density

APA Style

Anatolii Kuzmin, Leonid Grekov, Nataliia Kuzmina, Oleksii Petrov. (2022). About One Approach to the Construction of Clustering and Classification Grid-Type Algorithms. American Journal of Remote Sensing, 10(2), 30-38. https://doi.org/10.11648/j.ajrs.20221002.11

ACS Style

Anatolii Kuzmin; Leonid Grekov; Nataliia Kuzmina; Oleksii Petrov. About One Approach to the Construction of Clustering and Classification Grid-Type Algorithms. Am. J. Remote Sens. 2022, 10(2), 30-38. doi: 10.11648/j.ajrs.20221002.11

AMA Style

Anatolii Kuzmin, Leonid Grekov, Nataliia Kuzmina, Oleksii Petrov. About One Approach to the Construction of Clustering and Classification Grid-Type Algorithms. Am J Remote Sens. 2022;10(2):30-38. doi: 10.11648/j.ajrs.20221002.11

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