Identification of dynamically homogeneous areas with time series segmentation of remote sensing data
Plotnikov D.E., Kolbudaev P.A., Bartalev S.A.

 

Space Research Institute of Russian Academy of Sciences, Moscow, Russia

 PDF

Abstract:
We propose a method of segmentation of remote sensing time series data, which exploits multi-temporal information to identify objects’ boundaries. Extracting homogeneous objects with similar temporal behavior, the method analyzes large volumes of multi-temporal input data in a piecewise way and produces a consistent output segmentation layer for large territories. Segment building logic is simplified to minimize the computation time, while objects’ boundary identification accuracy remains sufficient for remote monitoring and mapping of vegetation, and specifically, agricultural crops. At the Space Research Institute of the RAS, the proposed method is currently applied for automated on-line satellite imagery analysis for recognition and mapping of (winter and spring) crops on large territories and land-use evaluation. The method successfully deals with gaps in remote sensing time series data and performs well even when input images are contaminated with speckle noise. Due to its  ability to map dynamically homogeneous surface areas with partially missing data, the method provides a potential for their recovery.

Keywords:
image segmentation, remote sensing, spectro-temporal metrics, image analysis.

Citation:
Plotnikov DE, Kolbudaev PA, Bartalev SA. Identification of dynamically homogeneous areas with time series segmentation of remote sensing data. Computer Optics 2018; 42(3): 447-456. – DOI: 10.18287/2412-6179-2018-42-3-447-456.

References:

  1. Dey V, Zhang Y, Zhong M. A review on image segmentation techniques with remote sensing perspective. Proc ISPRS TC VII Symposium 2010; XXXVIII(7A): 31-42.
  2. Gonzalez RC, Woods RE. Digital image processing. 3rd ed. Upper Saddle River, NJ: Pearson Prentice Hall; 2008. ISBN: 978-0-13-168728-8.
  3. Sarmah S, Bhattacharyya DK. A grid-density based technique for finding clusters in satellite image. Pattern Recognition Letters 2012; 33(5): 589-604. DOI: 10.1016/j.patrec.2011.11.021.
  4. Bartalev SA, Khovratovich TS. Assessment of satellite images segmentation methods for forest change detection [In Russian]. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa 2011; 8(1): 44-62.
  5. Rashedi E, Mirzaei A. A hierarchical clusterer ensemble method based on boosting theory. Knowledge-Based Systems 2013; 45: 83-93. DOI: 10.1016/j.knosys.2013.02.009.
  6. Yuan J, Wang DL, Li R. Remote sensing image segmentation by combining spectral and texture features. IEEE Transactions on Geoscience and Remote Sensing 2014; 52(1): 16-24. DOI: 10.1109/TGRS.2012.2234755.
  7. Deng H, Clausi DA. Unsupervised image segmentation using a simple MRF model with a new implementation scheme. Patt Recogn 2004; 37(12): 2323-2335. DOI: 10.1016/j.patcog.2004.04.015.
  8. Zhang J, Tan T. Brief review of invariant texture analysis methods. Patt Recogn 2004; 35(3): 735-747. DOI10.1016/S0031-3203(01)00074-7.
  9. Fralenko VP. Methods of image texture analysis, Earth remote sensing data processing [in Russian]. Program systems: Theory and applications 2014; 4(22): 19-39.
  10. Neubert M, Herold H. Assessment of remote sensing image segmentation quality. Proceedings GEOBIA 2008.
  11. Gang L. Remote sensing image segmentation with probabilistic neural networks. Geo-spat Inf Sci 2005; 8(1): 28-32. DOI: 10.1007/BF02826988.
  12. Su T, Zhang S. Local and global evaluation for remote sensing image segmentation. ISPRS Journal of Photogrammetry and Remote Sensing 2017; 130: 256-276. DOI10.1016/j.isprsjprs.2017.06.003.
  13. Medvedeva EV, Kurbatova EE. Method of textural segmentation of images based on Markov casual fields [In Russian]. Digital images processing 2012; 3: 76-80.
  14. Plotnikov DE. The method for time series segmentation of remote sensing images [In Russian]. Proceedings of the conference “Current problems in remote sensing of the earth from space” 2014: 375.
  15. Bartalev SA, Egorov VA, Zharko VO, Loupian EA, Plotnikov DE, Khvostikov SA. Current state and development prospects of satellite mapping methods of Russia’s vegetation cover [In Russian]. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa 2015; 12(5): 203-221.
  16. Plotnikov DE, Bartalev SA, Loupian EA, Tolpin VA. Accuracy assessment for winter crops mapping in spring-summer growing season with MODIS data [In Russian]. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa 2017; 14(4): 132-145. DOI: 10.21046/2070-7401-2017-14-4-132-145.
  17. Loupian EA, Bartalev SA, Krasheninnikova YuS, Plotnikov DE, Tolpin VA. Observation of early development of winter crops in spring 2017 in southern regions of Russia based on remote sensing data [In Russian]. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa 2017; 14(2): 268-272. DOI: 10.21046/2070-7401-2017-14-2-268-272.
  18. Loupian EA, Bartalev SA, Krasheninnikova YuS, Plotnikov DE, Tolpin VA. Abnormal development of spring crops in European Russia in 2017 [In Russian]. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa 2017; 14(3): 324-329. DOI: 10.21046/2070-7401-2017-14-3-324-329.
  19. Bartalev SA, Plotnikov DE, Loupian EA. Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique. Remote Sensing Letters 2016; 7(3): 269-278. DOI: 10.1080/2150704X.2015.1130874.
  20. Plotnikov DE, Kolbudaev PA, Bartalev SA, Loupian EA. Automated annual cropland mapping from reconstructed time series of Landsat data [In Russian]. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa 2018; 15(2): 112-127. DOI: 10.21046/2070-7401-2018-15-2-112-127.
  21. Plotnikov DE, Kolbudaev PA, Bartalev SA. Recognition of arable land based on seasonal time series of reconstructed Landsat imagery with Moscow region as an example [In Russian]. Proceedings of the conference “Current problems in remote sensing of the earth from space” 2017: 410.
  22. Cousty J, Bertrand G, Najman L, Couprie M. Watersheds, minimum spanning forests, and the drop of water principle. IEEE Trans Pattern Anal Mach Intell 2009; 31(8): 1362-1374. DOI: 10.1109/TPAMI.2008.173.
  23. Shi J, Malik J. Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 2000; 22(8): 888-905. DOI: 10.1109/34.868688.
  24. Grady L. Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 2006; 28(11): 1768-1783. DOI: 10.1109/TPAMI.2006.233.
  25. Grady L, Schwartz EL. Isoperimetric graph partitioning for image segmentation. IEEE Trans Pattern Anal Mach Intell 2006; 28(3): 469-475. DOI: 10.1109/TPAMI.2006.57.
  26. Lee JS. Speckle analysis and smoothing of synthetic aperture radar images. Computer Graphics and Image Processing 1981; 17: 24-32. DOI: 10.1016/S0146-664X(81)80005-6.
  27. Loupian EA, Proshin AA, Burtsev MA, Balashov IV, Bartalev SA, Efremov VYu, Kashnitskiy AV, Mazurov AA, Matveev AM, Sudneva OA, Sychugov IG, Tolpin VA, Uvarov IA. IKI center for collective use of satellite data archiving, processing and analysis systems aimed at solving the problems of environmental study and monitoring [In Russian]. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa 2015; 12(5): 263-284.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20