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The project on multilevel system of Earth operational hyperspectral monitoring is oriented towards development of regional system of operational hyperspectral.

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Presentation on theme: "The project on multilevel system of Earth operational hyperspectral monitoring is oriented towards development of regional system of operational hyperspectral."— Presentation transcript:

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2 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 Detection of chemical composition Database of spectral signatures LevelFunction 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»

4 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 Basic specifications of Resurs-P spacecraft equipment High-resolution equipment Swath, km (with altitude 475 km) 38 Spectral ranges,  m - panchromatic band 0,58 – 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 1 - narrow spectral bands 3 – 4 Wide-swath equipment Swath, km 97/441 Spectral ranges,  m - panchromatic band 0,43 – 0,7 - narrow spectral bands 0,43 – 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 12/60 - narrow spectral bands 24/120 Hyperspectral equipment Swath, km 30 Spectral range,  m 0,4 – 0.96 Number of spectral bands 192 Ground resolution in nadir, m 30

6 FeatureValue Payload (without taking pilot’s weight), kg470 Range of cruising speed, km/h Operating altitude, mдо 6000 Distance of flight, km1990 Flight endurance, h7,5 HSI specification VNIRSWIR Spectral range, nm Spectral resolutions, nm 3,510 Spatial resolutions, m 1,47 (Н=1000 м) Number of spectral bands Swath, m580 (H=1000 m)

7 Database of spectral signatures Detection of chemical composition

8 - vegetation - road surface - water surface Hyperspectral image Segmentation of objects Database of spectral characteristics - rooftops Spectral characteristics of selected objects of etalons 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

9 Different content of the pits Space image RGB composite according to the HSI Identifying the boundaries of regions with different concentrations of pollutants

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

11 Landfill Sand Contaminated water Metal roof Vegetation Dirt road Soil RGB – composite Classification result

12 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 Remote sensing market in North America Hyperspectral data195,5204,9214,4221,9229,4 Multispectral data218,0233,0248,0258,5269,0 Total:413,5437,9462,4480,4498,4 * Expert’s forecast of the American Society for Photogrammetry and Remote Sensing Charles Mondello and George Hepner.

14  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").

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