Land cover changes analysis based on deep machine learning technique

Journal of Automation and Information Sciences

Volumn 48, Issue 5, 2016, Pages 42-54

  Kussul, N N ^a   Lavreniuk, N S ^a   Shelestov, A Y ^b   Yailymov, B Ya ^a   Butko, I N ^c  

^a INSTITUTE OF HYDROBIOLOGY   (Ukraine)

^b NATIONAL UNIVERSITY OF LIFE AND ENVIRONMENTAL SCIENCES OF UKRAINE   (Ukraine)

^c National Space Facilities Control and Test Center   (Ukraine)

Author keywords

Deep machine learning technique; Geospatial analysis methods; Land cover changes analysis; Large volumes satellite and spatial data; Neural network approach; Ukraine territory mapping.

Indexed keywords

AGRICULTURE; DEEP LEARNING; LEARNING ALGORITHMS; REMOTE SENSING;

ANALYSIS METHOD; CHANGE ANALYSIS; DEEP MACHINE LEARNING TECHNIQUE; GEO-SPATIAL ANALYSIS; GEOSPATIAL ANALYSE METHOD; LAND COVER CHANGE ANALYSE; LAND-COVER CHANGE; LARGE VOLUME SATELLITE AND SPATIAL DATA; LARGE VOLUMES; MACHINE LEARNING TECHNIQUES; NEURAL NETWORK APPROACH; NEURAL-NETWORKS; SATELLITE DATA; SPATIAL DATA; UKRAINE; UKRAINE TERRITORY MAPPING.;

MAPPING;

EID: 84981350581     PISSN: 10642315     EISSN: None     Source Type: Journal    
DOI: 10.1615/JAutomatInfScien.v48.i5.40     Document Type: Article

References (53)

1. Hinton G.E., Osindero S., Teh Y.W., A fast learning algorithm for deep belief nets, Neural computation, 2006, 18, No. 7, 1527-1554.
2. Deng L., Yu D., Deep learning: methods and applications, Foundations and Trends in Signal Processing, 2014, 7, No. 3-4, 197-387.
3. Ma Y., Wu H., Wang L., Huang B., Ranjan R., Zomaya A., Jie W., Remote sensing big data computing: Challenges and opportunities, Future Generation Computer Systems, 2015, 51, 47-60.
4. Ma Y., Wang L., Liu P., Ranjan R., Towards building a data-intensive index for big data computing - A case study of Remote Sensing data processing, Information Sciences, 2015, 319, 171-188.
5. Kussul N., Shelestov A., Basarab R., Skakun S., Kussul O., Lavreniuk M., Geospatial intelligence and data fusion techniques for sustainable development problems, CEUR Workshop Proceedings, 2015, 1356, 196-203.
6. Kussul N.N., Shelestov A.Yu., Grid-systems for Earth exploration tasks. Architecture, models and technologies [in Russian], Naukova dumka, Kiev, 2008.
7. Kogan F., Kussul N.N., Adamenko T.I., Skakun S.V., Kravchenko A.N., Krivobok A.A., Shelestov A.Yu., Kolotii A.V., Kussul O.M., Lavrenyuk A.N., Winter wheat yield forecasting: a comparative analysis of results of regression and biophysical models, Journal of Automation and Information Sciences, 2013, 45, No. 6, 68-81.
8. Zgurovsky M., Pankratova N., Systems analysis: Problems, methodology, applications [in Russian], Naukova dumka, Kiev, 2005.
9. Kussul N., Shelestov A., Skakun S., Grid and sensor web technologies for environmental monitoring, Earth Science Informatics, 2009, 2, No. 1-2, 37-51.
10. Shelestov A.Yu., Kussul N.N., Skakun S.V., Grid technologies in monitoring systems based on satellite data, Journal of Automation and Information Sciences, 2006, 38, No. 3, 69-80.
11. Kussul N., Skakun S., Shelestov A., Kussul O., Yailymov B., Resilience aspects in the sensor web infrastructure for natural disaster monitoring and risk assessment based on earth observation data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7, No. 9, 3826-3832.
12. Kussul N., Mandl D.P., Moe K., Mund J.P., Post J., Shelestov A., Skakun S., Szarzynski J., Van G., Langenhove M., Handy interoperable infrastructure for flood monitoring: SensorWeb, Grid and Cloud, Ibid, 2012, 5, No. 6, 1740-1745.
13. Kussul N., Shelestov A., Skakun S., Kravchenko O., Data assimilation technique for flood monitoring and prediction, Intern. J. Inform. Theory Appl., 2008, 15, No. 1, 76-84.
14. Mandl D., Frye S., Cappelaere P., Handy M., Policelli F., Katjizeu M., Van Langenhove G., Aube G., Saulnier J., Sohlberg R., Silva J.A., Kussul N., Skakun S., Ungar S.G., Grossman R., Szarzynski J., Use of the earth observing one (EO-1) satellite for the Namibia SensorWeb flood early warning pilot, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6, No. 2, 298-308.
15. Kussul N., Shelestov A., Skakun S., Kravchenko O., High-performance intelligent computations for environmental and disaster monitoring, Int. J. Information Technologies & Knowledge, 2009, 3, 135-156.
16. Kussul N., Shelestov A., Skakun S., Flood monitoring from SAR Data, NATO science for peace and security series c: environmental security, 2011, 19-29, DOI: 10.1007/978-90-481-9618-0-3.
17. Kussul N.N., Sokolov B.V., Zyelyk Ya.I., Zelentsov V.A., Skakun S.V., Shelestov A.Yu., Disaster risk assessment based on heterogeneous geospatial information, Journal of Automation and Information Sciences, 2010, 42, No. 12, 32-45.
18. Skakun S., Kussul N., Shelestov A., Kussul O., Flood hazard and flood risk assessment using a time series of satellite images: A case study in Namibia, Risk Analysis, 2014, 34, No. 8, 1521-1537.
19. Azarskov V.M., Blokhin L.N., Zhitetsky L.S., Kussul N.N., Robust methods of estimation, identification and adaptive control [in Russian], NAU, Kiev, 2004.
20. Kussul N., Shelestov A., Skakun S., Intelligent computations for flood monitoring, International Book Series Advanced Research in Artificial Intelligence, 2008, 2, 48-54.
21. Simon Haykin, Neural networks and learning machines (3rd edition), Prentice Hall, Upper Saddle River, New Jersey, 2008.
22. Kussul N.N., Skakun S.V., Shelestov A.Yu., Analysis of risk of emergency situations on the basis of satellite data. Models and technologies [in Russian], Naukova dumka, Kiev, 2014.
23. Kussul N.N., Skakun S.V., Shelestov A.Yu., Geospatial analysis of disaster risk [in Ukrainian], Naukova dumka, Kiev, 2014.
24. Rachkovskij D.A., Kussul E.M., DataGen: A generator of datasets for evaluation of classification algorithms, Pattern Recognition Letters, 1998, 19, No. 7, 537-544.
25. Rachkovskij D.A., Kussul E.M., Baidyk T.N., Building a world model with structure-sensitive sparse binary distributed representations, Biologically Inspired Cognitive Architectures, 2013, 3, 64-86.
26. Skakun S., Basarab R., Reconstruction of missing data in time-series of optical satellite images using self-organizing Kohonen maps, Journal of Automation and Information Sciences, 2014, 46, No. 12, 19-26.
27. Kussul N., Shelestov A., Skakun S., Technologies for satellite data processing and management within international disaster monitoring projects, In. Grid and Cloud Database Management Grid, Fiore S., Aloisio G. (eds.), Springer, 2011, 279-306.
28. Olofsson P., Foody G.M., Herold M., Stehman S.V., Woodcock C.E., Wulder M.A., Good practices for estimating area and assessing accuracy of land change, Remote Sensing of Environment, 2014, 148, 42-57.
29. Hansen M.C., Egorov A., Roy D.P., Potapov P., Ju J., Turubanova S., Kommareddy I., Loveland T.R., Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project, Remote Sensing Letters, 2011, 2, No. 4, 279-288.
30. United States Geological Survey, state access mode on 15.12.2014: http://earthexplorer.usgs.gov
31. Chander G., Markham B., Helder D., Summary of current radiometric calibration coefficients for Landsat MSS,TM,ETM,and EO-1 ALI sensors, Remote Sensing of Environment, 2009, 113, 893-903.
32. Warmerdam F., Brent H., Michael L.G. (eds.), The geospatial data abstraction library, Open Source Approaches in Spatial Data Handling, Springer, Berlin, 2008, 87-104.
33. The Lucas survey European statisticians monitor territory, state access mode on 15.12.2014: http://www.uni-mannheim.de/edz/pdf/eurostat/03/KS-AZ-03-001-EN-N-EN.pdf.
34. LUCAS, 2009, Land use and cover area frame survey, state access mode on 01.04.2015: http://ec.europa.eu/eurostat/documents/205002/208938/LUCAS2009-C1-Instructions-Revised 20130925.pdf/.
35. Snchez J.S., Barandela R., Marqus A.I., Alejo R., Badenas J., Analysis of new techniques to obtain quality training sets, Pattern Recognition Lett, 2003, 24, No. 7, 1015-1022.
36. Lavreniuk M., Kussul N., Skakun S., Shelestov A., Yailymov B., Regional retrospective high resolution land cover for Ukraine: methodology and results, International Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, 2015, 3965-3968, DOI: 10.1109/IGARSS.2015.7326693.
37. Kussul N., Lupian E., Shelestov A., Skakun S., Tishchenko Y., Hluchy L., Determination of inundated territories on the basis of integration of heterogeneous data, Journal of Automation and Information Sciences, 2007, 39, No. 12, 42-51.
38. Kussul N., Skakun S., Shelestov A., Kravchenko O., Gallego J.F., Kussul O., Crop area estimation in Ukraine using satellite data within the MARS project 2012, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2012, 3756-3759, DOI: 10.1109/IGARSS.2012.6350500.
39. Agarwal R., Ranjan P., Chipman H., A new Bayesian ensemble of trees approach for land cover classification of satellite imagery, Canadian Journal of Remote Sensing, 2014, 39, No. 6, 507-520.
40. Kussul N., Skakun S., Shelestov A., Kravchenko O., Kussul O., Crop classification in Ukraine using satellite optical and SAR images, Models & Analyses, 2013, 2, No. 2, 118-128.
41. Waldner F., Veron S., De Abeyllera D., Zhang M., Wu B., Plotnikov D., Bartalev S., Lavreniuk M., Skakun S., Kussul N., Le Maire G., Dupuy S., Jarvis I., Defourny P., Towards a set of agrosystemspecific cropland ming methods to address the global cropland diversity, Int. Journal of Remote Sensing, 2016, 37, No. 12, 1371-1393.
42. Bakan G.M., Kussul N.N., Fuzzy ellipsoidal filtering algorithm of static object state, Problemy upravleniya i informatiki, 1996, No. 5, 77-92.
43. Bakan G.M., Kussul N.N., Set-theoretic identification of linear objects in the class of fuzzy ellipsoidal sets, Avtomatika, 1990, No. 3, 29-40.
44. Kussul N., Lemoine G., Gallego J., Skakun S., Lavreniuk M., Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences, The International Geoscience and Remote Sensing Symposium (IGARSS ), 2015 IEEE International, 2015, 165-168, DOI: 10.1109/IGARSS.2015.7325725.
45. Kussul N.N., Shelestov A.Yu., Skakun S.V., Basarab R.M., Yaylimov B.Ya., Lavrenyuk N.S., Kolotoy A.V., Yashchuk D.Yu., The retrospective regional land cover map for Ukraine: Methodology of construction and analysis of the results [in Ukrainian], Kosmicheskaya nauka i tekhnologiya, 2015, 21, No. 3, 31-39.
46. Lavrenyuk N.S., Skakun S.V., Shelestov A.Yu., Yailymov B.Ya., Yanchevskii S.L., Yashchuk D.Yu., Kostecki A.M., Classification of large areas of land cover on retrospective satellite data, Kibernetika i sistemnyi analiz, 2016, 52, No. 1, 137-149.
47. Kussul N., Skakun S., Shelestov A., Kussul O., The use of satellite SAR imagery to crop classification in Ukraine within JECAM project, Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, 2014, 1497-1500, DOI: 10.1109/IGARSS.2014.6946721.
48. Kussul N., Skakun S., Shelestov A., Lavreniuk M., Yailymov B., Kussul O., Regional scale crop mapping using multi-temporal satellite imagery, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2015, 45-52, DOI: 10.5194/isprsarchives-XL-7- W3-45-2015.
49. Skakun S., Kussul N., Shelestov A., Kussul O., The use of satellite data for agriculture drought risk quantification in Ukraine, Geomatics, Natural Hazards and Risk, 2015, 26, 1-18, DOI: 10.1080/19475705.2015.1016555.
50. Gallego J., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kussul O., Efficiency assessment of using satellite data for crop area estimation in Ukraine, International Journal of Applied Earth Observation and Geoinformation, 2014, 29, 22-30.
51. Shelestov A., Kravchenko A., Skakun S., Voloshin S., Kussul N.N., Geospatial information system for agricultural monitoring, Cybernetics and Systems Analysis, 2013, 49, No. 1, 124-132.
52. Gallego J., Kravchenko A., Kussul N., Skakun S., Shelestov A., Grypych Yu., Efficiency assessment of different approaches to crop classification based on satellite and ground observations, Journal of Automation and Information Sciences, 2012, 44, No. 5, 67-80.
53. Skakun S., Kussul N., Shelestov A., Lavreniuk M., Kussul O., Efficiency assessment of multitemporal C-Band Radarsat-2 Intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, No. 99, 1-8, DOI: 10.1109/JSTARS.2015.2454297.