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Capabilities

Solving problems in one information system
  • Full cycle of remote sensing data processing and creation of vector maps (from primary processing to thematic GIS projects).
  • Combination of all types of data in one coordinate space, recalculation of geographical projections.
  • Orientation to a complex solution of problems, the ability to save the entire project in one file.
  • Creation of geographic databases based on DBMS, possibility of integration with other systems.
  • Development and preservation of custom algorithms for automatic processing of remote sensing data.
  • Processing hundreds of gigabytes of information in streaming mode and forming seamless coverages for given areas.
Loading data

Opening images of 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, Santinel-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, Saninel 1A,B, SLC converted data, RCM manifest

Primary processing of materials with spacecraft "Resurs-P" №1,2,3, «Kanopus-V», «BKA»
80

routes per day

560

thousand km2 coverage area

800

GB for 1 survey route

Technologies for primary and additional processing of materials were implemented within the framework of the IMC PC space photography, including from the spacecraft "Resurs-DK", "Kanopus-V", "BKA" and "Resurs-P" No. 1, 2, 3. Primary & nbsp; processing of remote sensing data includes geometric, radiometric correction of the image, georeferencing of the image. Additional processing allows improve the quality of output products by increasing the spatial resolution of images, color correction, georeference refinement, etc.

Resurs-P

"Resurs-P No. 1 was launched on June 25, 2013 from the Baikonur Cosmodrome, accepted into regular operation on September 30, 2013.
"Resurs-P" #2 launched on December 26 2014 from the Baikonur Cosmodrome.
«Resurs-P» No. 3 was launched on May 13, 2016 with Baikonur Cosmodrome.
Lead developer: JSC RCC Progress.
Operator: NTs OMZ OAO Russian space systems.

The spacecraft has the capabilities of object and route surveys. Possible stereo survey of routes 115 km in size; survey of sites up to 100x300 km.

"Resurs-P is designed to update maps, provide economic activities of the Ministry of Natural Resources of Russia, the Ministry of Emergency Situations of Russia, Rosselkhoz, Rosrybolovstvo, Roshydromet and others consumers, as well as obtaining information in the field of control and environmental protection.

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Optoelectronic equipment of highly detailed resolution.
Characteristic Panchromatic channel Multispectral channel
Survey strip width, km 38
Spatial resolution in nadir, m 0,9 3-4
Spectral ranges, µm 0,58÷0,80 blue (0.45÷0.52)
green (0.52÷0.60)
red (0.61÷0.68)
red 1 (0.67÷0.70)
red 2 (0.70÷0.73)
red + near IR (0.70÷0.80)
Wide-angle multispectral equipment of high and medium resolution
Characteristic SHMSA-VR SHMSA-SR
Panchromatic channel Multispectral channel Panchromatic channel Multispectral channel
Survey strip width, km 97 441
Spatial resolution in nadir, m 12 23 60 120
Spectral ranges, µm 0,43÷0,70 blue (0.43÷0.51)
green (0.51÷0.58)
red (0.60÷0.70)
near IR 1 (0.70÷0.80)
near IR 2 (0.80÷0.90)
0,43÷0,70 blue (0.43÷0.51)
green (0.51÷0.58)
red (0.60*0.70)
Near IR 1 (0.70*0.80)
Near IR 2 (0.80*0.90)
Hyperspectral equipment
Characteristic GSA
Survey strip width, km 22
Spatial resolution in nadir, m 30
Spectral ranges, µm 0.4*1.1 (up to 256 spectral channels)
Kanopus-V, BKA

«Canopus-B» No. 1 - Russian spacecraft operational monitoring of man-made and natural emergencies. Launched July 22, 2012 with Baikonur Cosmodrome. Data received from Canopus-V contains RPC polynomials tool to improve image accuracy and speed up processing data.

"BKA" (Belarusian spacecraft) launched together with Russian satellite "Kanopus-V", has identical technical characteristics.

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Characteristic Panchromatic channel Multispectral channel
Survey strip width, km 23 20
Spatial resolution in nadir, m 2,5 12
Spectral ranges, µm 0,58÷0,86 blue (0.45÷0.52)
green (0.51÷0.6)
red (0.61÷0.69)
near IR (0.75÷0.84)
Processing scheme in PC IMC
Channel mixing

An example of the result of mixing channels in the IMC software package is given.

In the lower left corner is the original image, and in the upper right - the result of processing.

Binding Refinement

An example of refining the binding of images in the IMC software package is given.

Initial data: routes from the Belarusian spacecraft of the Canopus type. The most obvious discrepancy routes can be seen on the road. And this discrepancy in mutual binding is about 100 m.

Preliminary processing

Pre-processing in the IMC PC includes:

  • reading the passport of the picture;
  • formation of a composite image;
  • assigning color channels;
  • atmospheric image correction;
  • removal of non-informative fields;

The image passport is contained in the initial data set in text format or in the format xml. In the IMC PC, data from the passport is read automatically with the subsequent formation of a composite Images.

Information about the channels of the image is also automatically filled in: wavelength ranges, band width, gain, shift. And metadata: name of the spacecraft, time/date of shooting, sensor type, layer type, resolution, cloudiness

All meta-information is used in the subsequent processing and analysis of remote sensing data.

Before thematic processing, atmospheric correction is carried out according to the atmospheric transmission graph, which You can choose from the proposed list or download the required one.

The figure shows an image from the Landsat-8 spacecraft and an average graph of atmospheric transmission. Left image before atmospheric correction, on the right - after.

Pansharpening

The figure shows images from the spacecraft "Resurs-P" No. 1:

  • panchromatic image;
  • multispectral image;
  • result of pansharpening.

Pansharpening is the formation of a complexed image in natural colors with panchromatic image resolution.

The figure shows images & nbsp; from the WorldView-3 spacecraft:

  • panchromatic image;
  • multispectral image;
  • result of pansharpening.
Forming a seamless mosaic

An example of the formation of a seamless mosaic from several routes from the Resurs-P spacecraft is given. and Kanopus-V.

Source routes may differ in color, georeferencing, and linear resolution on the ground. The same tool refines the georeferencing for all routes, performs color correction and conducts boundary between routes so that it is invisible.

Linear Resolution Enhancement

The result of increasing the linear resolution is presented.

On the left is the original image, which has a resolution of 1 m.

As a result of processing, it was possible to increase the resolution to 0.57 m.

Thematic processing

Methods of thematic processing of satellite images allow you to study images in detail and receive vector layers with attribute information, including in automatic mode. In The IMAGE MEDIA CENTER software package implements a wide range of tools for thematic processing of satellite images. 

1. Image analysis in pseudocolors

Image analysis in false colors includes:

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

The image shows the original image from Landsat-8 to the territory Republic of Sakha. In the second window – image obtained in pseudo colors (7-6-4).

2. Formation and analysis of index images

The brightness value of each pixel of the index image is formed by performing mathematical operations in which values are used as parameters the brightness of each pixel from different channels of the image.
Depending on the purpose of the study different indexes are used:

  • vegetation indices;
  • soil indices;
  • water indices;
  • snow indexes;
  • custom indexes.

For the formation of index images in the IMC PC, the tool "Calculator channels", which allows the user to compose any mathematical formulas with using image channels.

To calculate the surface temperature a number of auxiliary calculations are performed (spectral radiation intensity, surface brightness temperature, spectral emissivity), conversion of values ​​to degrees Celsius and a universal temperature scale is used. To detect thermal anomalies, calculation of surface temperature, selection of areas of anomalous temperatures, vectorization objects and filling with attributive information.

3. Clustering (classification without training given k-means)

Before the start of clustering, it is not known how many and what objects are in the image, and after clustering, it is necessary to decipher the resulting classes in order to determine whether what objects they correspond to. Thus, classification without training is applied:

  • if it is not known in advance which objects are in the picture;
  • the image contains a large number of objects (more than 30) with complex boundaries;
  • can also be used as a preliminary step before supervised classification.

For supervised classification, reference areas are used, which defines an operator based on their belonging to a certain class of objects. For the next recognition as training samples, the values ​​of the pixels of the reference areas in different spectral ranges. Thus, each pixel of the image belongs to to a certain class based on sequential comparison with all created standards. At controlled classification, information classes are first determined, and then, based on them, spectral.

The image shows the result of the classification of the underlying surfaces with image training of "Resurs-P" spacecraft, "KShMSA-SR" equipment.

4. Classification with training

For supervised classification, reference areas are used, which defines an operator based on their belonging to a certain class of objects. For the next recognition as training samples, the values ​​of the pixels of the reference areas in different spectral ranges. Thus, each pixel of the image belongs to to a certain class based on sequential comparison with all created standards. At controlled classification, information classes are first determined, and then, based on them, spectral.

The image shows the result of the classification of the underlying surface with the training of the image of the Resurs-P spacecraft, the KShMSA-SR equipment.

5. Spectral analysis

The main quantities to be changed in the spectral analysis are wavelength, intensity of the reflected signal and the spatial coordinate of the studied surfaces. The IMC software implements the following spectral analysis methods:

  • correlation with/without regard to amplitude;
  • binary encoding;
  • spectral-angular mapping;
  • orthogonal projection of a subspace.

The figure shows a hyperspectral image from the Resurs-P spacecraft, spatial spectrogram built by a row of pixels, as well as spectral graphs, received from various types of objects.

Working with vector objects

Working with vector data within the IMC software includes creating and assigning styles for displaying vector objects, as well as the formation of classifiers taking into account the types of objects, display scale and a set of attribute data.

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

Vector layers can be saved in international formats SHP and TAB, as well as in the internal IMF format, which allows you to store raster and vector layers with attribute information in a single document.

Cataloging source images

The IMC has a data cataloging functionality that is designed to store and manage large amounts of data.

Search options:

  • Event

  • Period

  • Territory

Reports

For a comprehensive assessment of the results of thematic processing in the IMC software, the ability to generate reports that may contain: images, thematic maps, a legend, graphs, charts, etc.

The image shows a report generated by the results of updating forests and forest plots based on images from the Landsat-8 spacecraft to the territory of the Kirovskaya area.

The functionality of the IMC PC allows you to create report templates that are convenient for be used to demonstrate the results of a specific task with different inputs data, for example, during multi-temporal monitoring.