Complex analysis and monitoring of the environment based on earth sensing data
Lebedev L.I., Yasakov Yu.V., Utesheva T.H., Gromov V.P., Borusjak A.V., Turlapov V.E.

N.I. Lobachevsky National Research State University, 903950, Gagarin Avenue 23, Russia, N.Novgorod

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Abstract:
We study a problem of complex analysis and monitoring of the environment based on Earth Sensing Data, with the emphasis on the use of hyperspectral images (HSI), and propose a solution based on developing algorithmic procedures for HSI processing and storage. HSI is considered as a two-dimensional field of pixel signatures. Methods are proposed for evaluating the similarity of a HSI pixel signature with a reference signature, via simple alignment transformations: identical; amplitude scaling; shift along y-axis; and a combination of the last two. A clustering / recognition method with self-learning is proposed, which determines values of the transformation parameters that ensure the alignment of the current pixel signature with the reference signature. Similarity with the reference is determined by a standard deviation value. A HSI compression method with controlled losses has been proposed. The method forms a basis via accumulating reference signatures and represents the rest of the signatures by parameters matching them with the already detected class-reference signature. In an experiment with the GSI f100520t01p00-12 data of the AVIRIS spectrometer, the method provided a 2 % loss and compression coefficients of the original HSI ranging from 43 to 165 for various types of alignment transformation, while not requiring archiving and thus maintaining active access to the HSI and using the list of references as an analogue of the HSI palette. An algorithm for the formation of dense groups of detectable objects (for example, oil spots) and their nonconvex contouring, controlled by 4 parameters, is proposed.
A pilot version of the concept of geographic information system (GIS) and an appropriate database management system (DBMS) was built and implemented, which provides monitoring and is based on the prioritized processing and storage of the HSI, which serve as a data source for the system. A laboratory complex with new algorithms for processing and storing the GSE is introduced into the structure of the system .

Keywords:
hyperspectral images, image processing, self-learning recognition, lossy compression, compression without archiving, non-convex contouring, digital maps, DBMS, environmental monitoring.

Citation:
Lebedev LI, Yasakov YuV, Utesheva TSh, Gromov VP, Borusjak AV, Turlapov VE. Complex analysis and monitoring of the environment based on Earth sensing data. Computer Optics 2019; 43(2): 282-295. DOI: 10.18287/2412-6179-2019-43-2-282-295.

References:

  1. Vorobiova NS, Sergeyev VV, Chernov AV. Information technology of early crop identification by using satellite images [In Russian]. Computer Optics 2016; 40(6): 929-938. DOI: 10.18287/2412-6179-2016-40-6-929-938.
  2. Zhukov DV. Technique of thematic processing of hyperspectral data to assess the ecological status of water ports [In Russian]. Earth exploration from space 2014; 1: 66-71. DOI: 10.7868/S0205961414010084.
  3. Grigorieva OV, Zhukov DV, Markov AV, Saidov AG. Method for evaluating level of water area pollution by hyperspectral space sounding [In Russian]. Pat RF of Invent N 2616716 of May, 2017, Russian Bull of Inventions N11, 2017.
  4. Aerial surveillance of marine oil spills. Methodological guide for personnel involved in incident control and emergency response: Report IOGP N518 [in Russian]. Source: áhttp://www.oilspillresponseproject.org/wp-content/ uploads/2017/04/Aerial-Observation_Russian_V2.pdfñ.
  5. Guryanova AO, Ramenskaya EV, Mandra AG, Ermakov VV. Obtaining reference spectral signatures for hyperspectral imaging [in Russian]. Ecology and Industry of Russia 2014; 10: 44-48. DOI: 10.18412/1816-0395-2014-10-44-47.
  6. Ramenskaya EV, Kuznetsov MP, Ermakov VV, Barkova OR, Bran AA. Hyperspectral image classification using cluster data structure [in Russian]. Current Problems in Remote Sensing of the Earth from Space 2017; 14(7): 9-19. DOI: 10.21046/2070-7401-2017-14-7-9-19.
  7. Myasnikov EV. Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches. Computer Optics 2017; 41(4): 564-572. DOI: 10.18287/2412-6179-2017-41-4-564-572.
  8. Sirota AA, Dryuchenko MA. Generalized image compression algorithms for arbitrarily-shaped fragments and their implementation using artificial neural networks. Computer Optics 2015; 39(5): 751-761. DOI: 10.18287/0134-2452-2015-39-5-751-761.
  9. Zamyatin AV, Sarinova AZh. An algorithm for compressing hyperspectral aerospace images with the account of inter-band correlation. Applied Informatics 2013; 5(47): 35-42.
  10. Kopenkov VN, Myasnikov VV. Development of an algorithm for automatic construction of a computational procedure of local image processing, based on the hierarchical regression. Computer Optics 2016; 40(5): 713-720. DOI: 10.18287/2412-6179-2016-40-5-713-720.
  11. Gashnikov MV. Interpolation based on context modeling for hierarchical compression of multidimensional signals [in Russian]. Computer Optics 2018; 42(3): 468-475. DOI: 10.18287/2412-6179-2018-42-3-468-475.
  12. Kukharev GA, Shchegoleva NL. Methods of two-dimen­sional projection of digital images into eigen-subspaces: peculiarities of implementation and application [In Russian]. Computer Optics 2018; 42(4): 637-656. DOI: 10.18287/2412-6159-2018-42-4-637-656.
  13. Lee C, Langrebe DA. Feature extraction based on decision boundaries. IEEE Transactions on Pattern Analysis and Machine Intelligence 1993: 4(15): 388-400. DOI: 10.1109/34.206958.
  14. Vasin YuG, Yasakov YuV. GIS Terra: A graphic database management system. Pattern Recognition and Image Analysis 2004; 14(4): 579-586.
  15. Vasin YuG, Yasakov YuV. Distributed database management system for integrated processing of spatial data in a GIS [In Russian]. Computer Optics 2016; 40(6): 919-928. DOI: 10.18287/2412-6179-2016-40-6-919-928.
  16. Pakhomov PA, Borusyak AV, Turlapov VE. Investigation of hyperspectral image pixel signatures by the empirical mode decomposition method. CEUR Workshop Proceedings 2018; 2210: 352-364.
  17. Lebedev LI, Shahlan AO. Spatial analysis of hyperspectral images [In Russian]. Proceedings of the 28th International Scientific Conference on Computer Graphics and Machine Vision 2018: 315-318.
  18. Lebedev LI. Recognition and classification of objects HSI [In Russian]. Proceedings of the V International Scientific Conference "Regional Problems of Remote Sensing of the Earth" 2018: 138-143.
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