Аннотации:
The article presents an analysis
of a non-standard approach to the
segmentation of textural areas in
aerospace images. The question of the
applicability of sets of textural features for
the analysis of experimental data is being
investigated to identify characteristic
areas on aerospace images that in the
future it will be possible to identify types
of crops, weeds, diseases, and pests.
The selection of suitable algorithms was
carried out and appropriate software
tools were created on Matlab 2021a and
in the software package for statistical
analysis Statistica 12.
The main way to extract information
is to decrypt images, which are the
main carrier of information about the
underlying surface. The main tasks of
texture area analysis include selection and
formation of features describing textural
differences; selection and segmentation of
textural areas; classification of textural
areas; identification of an object by
texture.
To solve the tasks, spectral brightness
coefficient (SBC), Normalized Difference
Vegetation Index (NDVI), textural
features of various crops and weeds. Much
attention will be paid to the development
of software tools that allow the selection
of features describing textural differences
for the segmentation of textural areas
into subdomains. That is the question
of the applicability of sets of textural
features and other parameters for the
analysis of experimental data to identify
types of soils and soils, vegetation types,
humidity, crop damage in aerospace
images will be resolved.
This approach is universal and has
great potential for identifying objects
using image clustering. To identify
the boundaries of areas with different
properties of the image under study,
images of the same surface area taken at
different times are considered
