1. Earth operational hyperspectral monitoring
Multilevel system of
Earth operational hyperspectral monitoring
A.V. Raschupkin, A.S. Egorov

2. Project «multilevel system of Earth operational hyperspectral monitoring»
The project on multilevel system of Earth operational hyperspectral monitoring is oriented towards development of regional system of operational hyperspectral monitoring of physicochemical conditions of natural and anthropogenic objects based on data obtained with hyperspectral sensors used in space, aviation and ground facilities in the interest of regional state authority and local government as well as in the interests of enterprises having different form of property to solve a wide range of socioeconomic and scientific tasks.

3. Multilevel system of Earth operational hyperspectral monitoring
Function
Space
Global observation of Earth surface using three type of equipments
Aviation
Operative high-resolution hyperspectral imaging. Synchronous under spacecraft imaging for calibration and validation remote sensing data from Resurs-P spacecraft
Ground
Probe fence of soil and water, their analyzing, forming and updating database of etalons of relations «spectral coefficient of surface reflection – chemical composition»
Detection of chemical composition
Database of spectral signatures

4. Subproject of multilevel system of Earth operational hyperspectral monitoring
Development and examination plane-laboratory equipped hyperspectral sensor (0,4 – 2,5 µm) and high-precision navigation equipment, attestation and certification it.
Development of hyperspectral
data found.
Development of system of stationary and
mobile physical and chemical laboratories.
Development of software of thematic processing Information, obtained from
the hyperspectral monitoring system.
Organization business structures for dissemination of remote sensing information.

5. Space facilities of Monitoring
Basic specifications of Resurs-P spacecraft equipment
High-resolution equipment
Swath, km
(with altitude 475 km)
Spectral ranges, m
- panchromatic band ,58 – 0,8
- narrow spectral bands 0,45 – 0,52
0,52 – 0,6
0,62 – 0,69
0,72 – 0,9
0,59 – 0,64
0,7 – 0,73
0,7 – 0,8
Ground resolution in nadir, m
(with altitude 475 km)
- panchromatic band
- narrow spectral bands – 4
Wide-swath equipment
Swath, km /441
Spectral ranges, m
- panchromatic band ,43 – 0,7
- narrow spectral bands ,43 – 0,51
0,51 – 0,58
0,60 – 0,70
0,70 – 0,90
0,80 – 0,90
Ground resolution in nadir, m
- panchromatic band /60
- narrow spectral bands /120
Hyperspectral equipment
Swath, km
Spectral range, m ,4 – 0.96
Number of spectral bands
Ground resolution in nadir, m

6. Aviation facilities of Monitoring
MAIN specification of «Rysachok» airplane
Aviation hyper spectral imager
Aisa Fenix
Feature
Value
Payload (without taking pilot’s weight), kg
470
Range of cruising speed, km/h
Operating altitude, m
до 6000
Distance of flight, km
1990
Flight endurance, h
7,5
HSI specification
VNIR
SWIR
Spectral range, nm
Spectral resolutions, nm
3,5 10
Spatial resolutions, m
1,47
(Н=1000 м)
Number of spectral bands
344
275
Swath, m
580
(H=1000 m)

7. ground facilities of monitoring
Database of spectral signatures
Detection of chemical composition
Mobile physical and chemical laboratories

8. Development of software of thematic processing information, obtained from the hyperspectral monitoring system
Identification of objects on the specified spectral characteristics
Hyperspectral image
Segmentation of objects
Spectral characteristics
of selected objects
Database
of etalons
of spectral
characteristics
COMPARIOSON OF SPECTRAL CHARACTERISTICS
Step 1. Segmentation of objects in a hyperspectral image based on correlation spectral characteristics
Step 2. Determination of spectral characteristics for the selected segments
Step 3. Comparison of the spectral characteristics of the selected segments with etalon spectral characteristics of the database based on determination of the spectral angle
Step 4. Classification of objects and the formation objects map
vegetation
road surface
water surface
rooftops

9. Monitoring of waste oil pits using hypespectral information
Classification content of waste oil pits
Different content of the pits
Space image
RGB composite
according to the HSI
Identifying the boundaries of regions with different concentrations of pollutants

10. Determination of pond’s depth with using methods of bathymetry
Three-dimensional model of the pond, built with the help of echo sounder’s measurements
Pond in area of Chubovka village
(Samara region)
Bathymetry map constructed by spectral characteristics of hyperspectral image

11. Monitoring of landfills and solid waste using hyperspectral data
RGB – composite
Classification result
Landfill
Contaminated water
Soil
Sand
Metal roof
Dirt road
Vegetation

12. Targets of hyperspectral monitoring
Hyperspectral information gives us opportunity to create thematic maps
and geo-information systems aimed to solve problems below:
 water quality monitoring in water basins and rivers, mechanic, chemical and biological contaminant evaluation;
 soil salinity classification;
 soil fertility and mineral composition evaluation;
 vegetation cover, agricultural and forest landscapes monitoring;
 narcotic plants identification;
 technogenic and natural dangerous substances emission and leakage monitoring, their consequences development forecasting;
 dumping sites detection, their content and size estimation.

13. Forecast the North American market of hyperspectral information (million Dollars) *
Remote sensing market in North America
2009
2011
2014
2017
2020
Hyperspectral data
195,5
204,9
214,4
221,9
229,4
Multispectral data
218,0
233,0
248,0
258,5
269,0
Total:
413,5
437,9
462,4
480,4
498,4
* Expert’s forecast of the American Society for Photogrammetry and Remote Sensing Charles Mondello and George Hepner.

14. The problem of mixing of the spectra ("mixed pixel").
The main problems in the sphere of hyperspectral information processing
The lack of highly qualified specialists in the field of processing and analysis of hyperspectral data;
The elimination of the influence of atmospheric conditions (atmospheric correction);
The absence of domestic spectral libraries of natural and man-made objects;
Identification of receptor portions of the spectrum and determination of the optimal spectral intervals for specific tasks;
The problem of mixing of the spectra ("mixed pixel").

15. Thank you for your attention!