Abstract:
The article presents a technique
for studying space images based on
the analysis of the spectral brightness
coefficient (SBC) of space images of
the earth's surface.
Recognition of plant species,
soils, and territories using satellite
images is an applied task that allows
to implement many processes in agriculture and automate the activities of
farmers and large farms. The main
tool for analyzing satellite imagery
data is the clustering of data that
uniquely identifies the desired objects
and changes associated with various
reasons.
Based on the data obtained in the
course of experiments on obtaining
numerical SBC values, the patterns of
behavior of the processes of reflection
of vegetation, factors that impede the
normal growth of plants, and the proposed clustering of the spectral ranges of wave propagation, which can be
used to determine the type of objects
under consideration, are revealed.
Recognition of these causes through
the analysis of SBC satellite images will create an information system
for monitoring the state of plants and
events to eliminate negative causes.
SBC data is divided into non-overlapping ranges, i.e. they form clusters
reflecting the normal development of
plant species and deviations associated with negative causes. If there are
deviations, then there is an algorithm
that determines the cause of the deviation and proposes an action plan to
eliminate the defect.
It should be noted that the distribution of the brightness spectra
depends on the climatic and geographical conditions of the plant species and is unique for each region.
This study refers to the Akmola
region, where grain crops are grown
