Features

 

Primary processing of Resurs-P1, 2, 3, Kanopus-V and BKA satellite data

80

scenes per day

560

thousands of km2coverage

800

GB per single scene

Technology of remote sensing data primary processing within IMC software is fully automated and allows user to perform the formation of the scenes for Resurs-DK, Resurs-P1, 2, 3, Kanopus-V1, BKA imagery. Primary processing contains following steps: geometrical correction of the scene using digital elevation model and RPC coefficients, radiometric correction, eliminating offset between spectral bands, formation of synthesized color image, and formation of fused image in natural colors with resolution of panchromatic image. Further processing such as spatial resolution improvement, color correction, georeferencing improvement, etc provides a possibility to significantly increase the quality of the output data

Resurs-P

Resurs-P1 was launched on the 25th of June 2013 from Baikonur Cosmodrome and accepted into regular operation on the 30th of September 2013.
Resurs-P2 was launched on the 26th of December 2014 from Baikonur Cosmodrome.
Resource-P3 was launched on the 13th of May 2016 from Baikonur Cosmodrome.
Developer: TsSKB Progress
Operator: NTs OMZ RSS.

The spacecraft is capable of photographing individual targets on the Earth surface, as well as long stretches of Earth surface extending as far as 2,000 kilometers. Resurs-P could also image areas 100 by 300 kilometers during a single pass and conduct stereo-imaging.

Resurs-P is designed for maps update, environmental control and protection; among the users of the satellite data there are following institutions: Ministry of Natural Resources, Ministry of Emergency Situations, Ministry of Transportation, Ministry of Agriculture, Ministry of Fishing, Ministry of Meteorology and other domestic and foreign customers.

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Optical-electronic high resolution equipment

Characteristics Panchromatic band Multispectral band
Swath width image, Km 38
Spatial resolution (at nadir), m 0,9 3-4
Spectral range, µm 0,58÷0,80 blue (0,45÷0,52)
green (0,52÷0,60)
красный (0,61÷0,68)
red 1 (0,67÷0,70)
red 2 (0,70÷0,73)
red + near IR (0,70÷0,80)

Wide-multispectral equipment of high and medium resolution

Characteristics ShMSA-VR ShMSA-SR
Panchromatic band Multispectral band Panchromatic band Multispectral band
Swath width image, km 97 441
Spatial resolution (at nadir), m 12 23 60 120
Spectral range, 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

Characteristics GSA
Swath width image, km 22
Spatial resolution (at nadir), m 30
Spectral range, µm 0,4÷1,1 (up to 256 spectral bands)

Kanopus-V1, BKA

Kanopus-V1 is a Russian spacecraft designed for real-time technogenic and natural disasters monitoring. It was launched on the 22th of July 2012 from Baikonur Cosmodrome. Data acquired from Kanopus-V1 spacecraft contain RPC-for faster image processing and image accuracy improvement.

BKA (Belorussian spacecraft) was launched together with Russian Kanopus-V1 and has identical characteristics.

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Characteristics Panchromatic band Multispectral band
Swath width image, km 23 20
Spatial resolution (at nadir), m 2,5 12
Spectral range, 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)

Data processing sequence in IMC software

Primary processing scheme   

 

Preliminary processing

•	Reading image’s metadata file   

Preliminary processing contains following steps:

  • reading image’s metadata file;
  • formation of multispectral image;
  • assigning color components;
  • image atmospheric correction;
  • removing uninformative areas;

Scene’s metadata file is stored in the data package along with the image in XML or TXT file formats. IMC provides a possibility to read metadata files and open related scenes automatically.

Characteristics of color bands such as range, width, gain, offset, along with the satellite and sensor type, surveying date and time, resolution, and information about cloud coverage will be read and filled automatically.

All of the information read from the metadata file is useful for further image processing.

Atmospheric correction is required for further thematic processing of the image. User can select one of listed atmosphere transmission plots or upload a new one.

Here you can see Landsat-8 image and averaged atmosphere transmission plot. On the left there is an original image, on the right – image after atmospheric correction was performed.

Result of atmospheric correction   

Pansharpening

Result of Pansharpening (WorldView-2)   

Image: Russian Federation, Chelyabinsk Oblast, Resurs-P1 (Geoton-L1)

Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution RGB image.

You can see images obtained from Resurs-P1 spacecraft:

  • panchromatic image;
  • multispectral image;
  • pansharpened image.

You can see images obtained from WorldView-3 spacecraft:

  • panchromatic image;
  • multispectral image;
  • pansharpened image.

Результат паншарпенинга (WorldView-3)   

Image: Australia, Sydney, WorldView-3

 

Thematic processing

Thematic processing methods provide a possibility to perform a detailed analysis of the satellite imagery and create vector maps filled with attributive information as the result of the processing both manually and automatically. There are various algorithms for remote sensing data thematic processing implemented within Image Media Center software.

1. False color image analysis

Input data of Resurs-P   

Image: Russian Federation, Sakha Republic, Landsat-8

False color image analysis includes following actions:

  • selecting different combinations of color components;
  • maximum likehood method;
  • using various color spaces (RGB, CMYK, Lab, HLS, HSB).

On the left image you can see the original image obtained from Landsat-8 satellite; on the right – false color image (bands 7-6-4).

2. Indices calculation and analysis

Pixel brightness values of index image are the result of mathematical operations on brightness values of each pixel for different color bands.

Various indices are used for different research purposes:

  • vegetation indices;
  • soil indices;
  • water indices;
  • snow and ice indices;
  • custom indices.

“Band calculator” tool is designed to create index images; it allows user to perform various mathematical operations on spectral bands.

To create a land surface temperature map a sequence of auxiliary calculations should be performed (such as spectral emissivity, surface brightness temperature, etc.), obtained values can be converted into various units of temperature; a universal color range is used to create a colorful land surface temperature map. Temperature maps allow user to detect areas of abnormal temperatures and discover wildfires and burned areas.

Input data of Resurs-P   

Image: Russia, Kamchatka Krai, Landsat-8

3. Clustering (unsupervised k-means classification)

Input data of Resurs-P   

Image: Russian Federation, St. Petersburg, WorldView-2

It is unknown in advance before the clustering which and how many types of objects are present on the image, thus after the image clustering it is necessary to identify the classes recognized by unsupervised classification. Unsupervised classification is usually applied in the cases, such as:

  • when it is unknown in advance which types of objects are present on the image;
  • when there is a large amount of objects (30<) with complex borders on the image;
  • auxiliary step before supervised classification.

4. Supervised classification

Supervised classification requires manual preparation where operator determines reference areas on the image for each class of objects. For classes recognition pixel brightness values of reference areas are used for each spectral band. Thus each pixel of the image will be assigned to a certain class as a result of consequential comparison with all selected references.

On the image you can see the result of the supervised classification based on Resurs-P ShMSA-SR data.

Input data of Resurs-P   

Image: India, Resurs-P, ShMSA-SR

5. Spectral analysis

Spectral analysis   

Image: Australia, Pert, Resurs-P (GSA)

The main values to be changed in the spectral analysis, is the wavelength, the intensity of the reflected signal and the spatial coordinates of investigated surface. IMC software provides the following methods of spectral analysis:

  • correlation with/without considering amplitude;
  • binary encoding;
  • spectral angle mapper;
  • orthogonal subspace projection.

On the image you can see hyperspectral image by Resurs-P satellite, spectrogram for the row of pixels, and spectral plots for different types of objects.

 

Working with vector data

IMC software allows user to create vector objects of any complexity (markers, lines, polygons, complex objects), create and assign custom display styles for vector objects, create vector classifiers for different types of objects, scale factor, and attribute data.

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

Vectoring   

 

Reports

Reports   

For comprehensive analysis of the results of the processing, statistical reports can be generated automatically. Reports can contain all types of visual representation of statistical data such as graphs, legends, information about the scene etc., as well as satellite images and thematic maps. User can create custom report drafts for convenient demonstration of the results of various thematic processing.

On the image you can see the report that demonstrates the forest cover change over the period of monitoring based on multi-temporal analysis of Landsat-8 images.