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Main features

Basic functionality of the software package

Remote sensing data processing

Support for images from various spacecraft:

Aist2D, EO-1, FORMOSat 2, Gaofen-2, GeoEye-1, IKONOS 2, Kanopus-B, Kanopus-1, Kanopus-2, Kanopus-3, Landsat 1, Landsat 2, Landsat 3, Landsat 4, Landsat 5, Landsat 6, Landsat 7, Landsat 8, Landsat 9, Meteor-M2, PlanetScope, Pleiades-1A, Pleiades-1B, QuickBird-2, RapidEye, Resurs-DK1, Resurs-P №1, Resurs-P №2, Resurs-P №3, Sentinel-2, Spot 5, Spot 6, Spot 7, Triplesat 1, Triplesat 2, Triplesat 3, VNREDSat 1A, WorldView-1, WorldView-2, WorldView-3.
Cosmo-SkyMED h5, Cosmo-SkyMED XML, TerrraSAR-X/TanDEM-X passport, Radarsat-2 Passport, Seninel 1A,B, SLC converted data, RCM manifest

Primary processing

Channel reconciliation

In the IMC software package it is possible to eliminate the offset between the channels of multispectral data. Removing the offset is accomplished by binding of spectral channels to panchromatic channel.

Elimination of channel misalignment in IMC PC. Image from the Kanopus-V satellite, Moscow, Luzhniki stadium. Left - original image, right - after processing.

Refinement of georeferencing

An example of image georeferencing refinement in IMC software package is given. Source data: routes from the Kanopus-V satellite. The most clearly visible divergence of the routes can be seen on the road. And this divergence is about 100 m according to mutual georeferencing. The first image shows the original image, the second image shows the reference points for subsequent image georeferencing, and the third image shows the image after processing.

Geodata georeferencing is refined using existing spatial data such as spatially referenced rasters or vector objects that have the correct coordinate system. This allows to link two images so that the same objects on these images are located by the same coordinates using reference points.

The software package includes the function of automatic search for linking points, as well as tools for independent set of reference points by the operator.

Preliminary processing

Preliminary processing

Preliminary processing in the IMC PC includes

  • reading the image passport;
  • composite image formation;
  • assignment of colour channels;
  • atmospheric correction of the image;
  • removal of uninformative fields.

Reading the image passport

The image passport is contained in a set of source data in text format or XML format. IMC reads data from the passport automatically and then generates a composite image. subsequent formation of a composite image.

Also, information about image channels is automatically filled in: wavelength ranges, bandwidths, gain, shift. And metadata: spacecraft name, time/date of acquisition, sensor type, layer type, resolution, cloud cover.

Atmospheric correction

The purpose of atmospheric correction is to obtain surface reflectivity (which characterises surface properties) from remote probes by removing atmospheric effects.

There are two ways to perform atmospheric correction in IMC: general correction and atmospheric model.

The general atmospheric correction is set manually by the operator if necessary parameters are available.

In the second method, atmospheric correction is performed according to the atmospheric transmittance graph, which can be selected from the proposed list or downloaded.

Atmospheric correction on Landsat-8 image, performed in IMC using transmission coefficient plot. On the left - original image, on the right - image after atmospheric correction.


The process of pansharpening in the image from the Kanopus-V spacecraft made in IMC. From left to right are shown: panchromatic image (2 m), multispectral image (10 m), pansharpening - as a result of merging (2 m).

Pansharpening allows to obtain one image from panchromatic and multispectral channels of two products. The panchromatic channel usually has higher spatial resolution, the multispectral channel has lower spatial resolution. Channel fusion results in a high-resolution color image.

Noise reduction

The IMC implements filtering methods used to eliminate fine details and noise representing sharp local brightness jumps in case the main objects to be analyzed are area objects.

Elimination of noise and scratches on an archive image in IMC software package.

Enhancement of linear resolution

Materials of Geoton-L1 satellite. The original image with 1 m resolution is shown on the left. As a result of processing it was possible to increase the resolution up to 0.57 m.

In the IMC software package there is a possibility to increase the linear resolution of images and to evaluate the quality of source materials.

Thematic processing

Satellite images thematic processing methods allow to study images in detail and obtain vector layers with attributive information, including in automatic mode. A wide range of tools for thematic processing of satellite images is implemented in IMAGE MEDIA CENTER software package.

Image analysis in pseudo-colors

Image analysis in pseudo-colors includes:

  • application of different combinations of color channels;
  • the method of greatest similarity;
  • use of color spaces (RGB, CMYK, Lab, HLS, HSB).

Image analysis in pseudo-colors is useful for detecting specific objects, such as detecting fires in images.

One of the thematic tasks that can be solved with the help of IMC is fire detection. On the left - original image from Landsat-8 satellite, on the right - image in pseudo-colors.

Index image formation and analysis

In IMC it is possible to calculate indexes for different thematic tasks. The vegetation index can be used to separate vegetation from other objects. On the left - original image from Santinel-2 satellite, on the right - index image.

The brightness value of each pixel of the index image is formed by means of mathematical operations, in which the brightness values of each pixel from different channels of the image are used as parameters.

Depending on the purpose of the study, different indices are used:

  • vegetation indices;
  • soil indices;
  • water indices;
  • snow indices;
  • user indices.

To form index images, the IMC PC implements the "Channel Calculator" tool, which allows the user to make any mathematical formulas using image channels.


If it is not known what objects are present in the original image, the IMC PC has a possibility to perform classification without training by two methods: k-means and fuzzy clustering.

The k-means clustering method is the most popular, its essence consists in: distribution of sample objects into clusters, recalculation of cluster centers.

The method of fuzzy clustering in which each data point can belong to more than one cluster.

After clustering, the resulting classes need to be decoded to determine which objects they correspond to.

Thus, classification without training is applied:

  • if it is not known in advance what objects there are in the image;
  • the image has a large number of objects (more than 30) with complex boundaries;
  • it can also be used as a preliminary stage before classification with training.
- seawater - water mixing - water - sand - suspended solids - soil - swamps - deep areas
In IMC it is possible to apply classification without training to solve one of the most common thematic tasks - object classification. On the left - original image from Santinel-2 satellite, on the right - thematic map with classified objects.

Classification with training

- trees - shrubs - grass - water - soil - sand - roadway - railroad tracks - buildings
Using classification with training, you can create thematic maps of the underlying surfaces in IMC.

For supervised classification, reference areas are used, which are defined by the operator based on their belonging to a certain class of objects. For subsequent recognition, pixel values of reference areas in different spectral ranges are used as training samples.

Thus, each pixel of the image belongs to a certain class based on a sequential comparison with all created standards. In supervised classification, first the information classes are determined and then, based on them, the spectral classes are determined.

Once the classes are set in IMC, it is possible to choose the method of classification:

  • method of shortest distance;
  • method of greatest similarity;
  • classification by Mahalanobis distance.

Spectral analysis

The main quantities to be changed during spectral analysis are wavelength, intensity of the reflected signal and spatial coordinate of the surface under study.

The following methods of spectral analysis are implemented in IMC:

  • correlation with/without amplitude consideration;
  • binary coding;
  • spectral-angle mapping;
  • orthogonal subspace projection.
- coniferous forest - plowed field - grass - soil - fields - crops
Spectral analysis can be useful for identifying objects, classifying data by their spectral characteristics of ground objects. For example, one of the most common tasks, which is solved by IMC PC, is classification of vegetation by species composition.

Texture analysis

ith the help of texture analysis in IMC, it is possible to determine some characteristics of the object under consideration. For example, on the image it is possible to distinguish different vegetation species. First - the original image, second - texture analysis, third - the result of the analysis, as a result of processing were separated tall trees from shrubs.

IMC provides an opportunity to solve a number of thematic tasks using texture analysis - a set of methods for qualitative and quantitative characterization of objects, processes and phenomena on the Earth surface and in the atmosphere, based on the study of their structural features on remote sensing data.

Texture is understood as a set of image features characterizing its degree of homogeneity, isotropy and regularity.

Vector data processing

Working with vector data within IMC includes creation and assignment of vector objects display styles, as well as formation of classifiers taking into account object types, display scale and attributive data set.

IMC functionality allows to create vector objects of any complexity (markers, lines, polygons, composite objects).

Vector layers can be stored in international formats SHP and TAB, as well as in internal IMF format, which allows storing raster and vector layers with attributive information in a single document.

In IMC PC it is possible to create maps with vector datasets. The image shows a vector map of the coast, Landsat-8 image, Krasnodar Territory.

Conventional signs

The created vector map in the IMC PC using conventional signs, a picture from the Landsat - 8 spacecraft, Murmansk region.

It is possible to create and edit any styles of symbols in IMC.

The software package allows you to edit conditional signs in many ways, such as:

  • color;
  • rotation angle;
  • size;
  • additional parameters such as glow, shadow, stroke, etc.

Buffer zones

In IMC, it is possible to add buffer zones according to standards, depending on the user's task.

You can create vector buffer zones for the security zone of power lines, railways, parking lots, gas pipelines, etc., depending on the specific task being solved, and there are also tools for creating a custom buffer zone with values set by the operator and the ability to build buffer zones of intersecting objects.

Buffer zones can help in solving the problem of monitoring the security zone of power lines. In IMC, it is possible to create buffer zones according to the standard for security zones of power lines, depending on the voltage.

Topology verification

- vector objects - the result of the topology verification
1) The result of the topology verification is the found self-intersection. 2) The result of the topology verification is the found overlap. 3) Topology verification result - void search found. 4) Topology verification result - search for hanging nodes in a given radius. 5) Topology verification result - filtering of voids

Topology verification can be performed both for all objects of the edited vector layer and for the current selection of vector objects. A new vector layer with detected errors is created by results of the verification.

Topology verification may include:

1) Search for polygon self-intersections: as a result of the check a vector layer with points in the places of polygon self-intersections is created.

2) Search for polygon overlaps: as a result of verification, a vector layer with polygonal objects detecting polygon overlaps is created.

3) Search for voids between polygons: as a result a vector layer with polygonal objects detecting voids between polygons is created.

4) Search for dangling nodes: the check results in creation of a vector layer with point objects detecting nodes located at a distance less than specified by the user relative to other linear objects of the layer.

5) The "Filter objects by area" section allows to filter out objects with area less than specified.

Density ranking of vector objects

Polygonal theme objects are represented by points, the number of which, multiplied by the point weight, corresponds to the value of the attribute selected for display on the map. It is used to demonstrate the area distribution of a phenomenon.

The tool is intended for drawing density maps of point objects of a vector layer. When the algorithm is finished, a raster layer with density values is created.

The image shows a point density map for a vector layer of buildings in the territory of the seaside town of Krasnodar Krai, made with the help of the algorithm for object density ranking.

Working with the attribute table

The result of thematic processing for solving the problem: identification of fire centers on Landsat-8 image is presented in the form of attribute table with areas of fire centers and their coordinates.

In IMC it is possible to create and edit attribute tables for vector objects. The table can be used to calculate the results of data processing. It is possible to create symbol, numeric, logical and other fields, as well as to create mathematical expressions, for example, to calculate area of vector objects.

In IMC there is a possibility to edit and search by attributes with the help of SQL queries.

Attribute information can be saved in .csv format.

Combining space images
with WMS, WFS services

In IMC there is a possibility to add a web layer to the document based on WMS and WFS data.

WMS service allows to load vector data, which contain information on state borders and administrative division, relief data, nature objects, topographic data, data on energy resources, etc.

WFS service allows to download vector data, which contain generalized information on demography of different countries and the whole world, borders of states and administrative division, relief data, topographic data, data on energy resources, etc.

The image shows a fragment of the route from the Kanopus-V satellite combined with a web layer based on data from the WMS service.

Data catalogization

Created database of space images and routes from different spacecrafts to the territory of the Russian Federation.

In IMC it is possible to create and store a database containing imagery routes and individual images, as well as information search functions in the electronic catalog, providing information selection by the required values.

Search parameters:

  • route;
  • spacecraft type;
  • equipment;
  • imaging date;
  • territory;
  • description;

Process automatization

A program algorithm (macro), a pre-recorded sequence of user actions that can be applied to different sets of input data. The macro mechanism is used for automated solution of similar tasks according to specified processing algorithms in IMC.

A recorded macro allows automatic thematic processing, from preliminary processing of space imagery materials to graphical and textual reports based on processing results.

After recording macros are saved in MCR format. Actions in the algorithm can be added, deleted, overwritten at any time.

  • possibility to save the algorithm in MCR format;
  • possibility of further editing of the macro;
  • working with relative paths;
  • running the macro in the server mode.
Show the scheme of macro algorithm operation
The image shows the stages of solving the thematic task: "Monitoring of water objects" in the course of macro execution. The image from Santinel-2 satellite, Omsk Region. The first image shows the original image, the second image shows the index image made in the course of the algorithm, and the third image shows the resulting map.

Example of algorithm operation

Flood and flood monitoring

In order to monitor flood-prone areas or model a flood zone, you can run a ready-made algorithm or create your own, for further solution of typical tasks.

The first step of the algorithm execution is to specify working directories - recording relative paths with which the macro will interact (opening and saving files).

The next stage is the preliminary processing of the image: opening of the passport, image correction, removal of uninformative fields and creation of the image boundary.

After pre-processing, thematic processing is performed. For flood modeling, an image is created, calculated using NDWI index from the archival and current image. A color range is applied to the index image. Using a selected range of values, water features are separated from other features.

An attribute table with area values is created for the vector water layer.

Additional vector objects with data on buildings, road network, socially significant objects and others are loaded in order to highlight areas that may be in the danger zone.

After thematic processing, the resulting map is created with the source image and received vector objects.

The resulting map data is used to generate a report with basic information obtained as a result of thematic processing: image boundary on the general map, resulting map, attribute table.

View a diagram of the algorithm for the topic problem
01 / 05
  • pre-processing, archive and current image
  • thematic processing, obtaining an index image of the current and archive image
  • thematic processing, application of color series
  • resulting map
  • report

Report generation

For comprehensive evaluation of thematic processing results, IMC provides the possibility to generate reports, which may contain: images, thematic maps, legend, graphs, diagrams, etc.

The report forms are intended for further printing and are presented in basic paper sizes (A4, A3, A2, 640 x 480, etc.), or any size specified by the operator.

It is also possible to generate a report in portrait and landscape orientation.

The report presents the result of thematic processing in the form of the generated report: modeling of flooding from Landsat-8 image, Kirov region.
The report presents the result of thematic processing in the form of a generated report: fire monitoring from Landsat-8 image, Madeira Island.

Reports in Image Media Center software complex can be both graphical and textual and can be saved in JPG, PDF, CSV formats.

IMC functionality allows creating report templates, which can be conveniently used to demonstrate the results of a specific task with different input data, for example, in the course of multi-temporal monitoring.