PDF

Abstract:
In this paper, we propose an earth remote sensing data processing technology for obtaining vegetation types maps. The technology includes the following steps: obtaining superpixel representation of an image, calculating superpixel features, K-Means clustering of superpixels by a user-defined training sample, and obtaining vegetation types maps. When compared to other solutions, the major difference of the proposed technology is the ability to combine superpixel segmentation and feature calculation into a single process in one pass of an image that reduces the computational complexity. Another difference lies in the way of forming a sample dataset using superpixel representation of an image. The advantages of the proposed technology are the use of a smaller training dataset and a higher classification quality in comparison with the elemental classification.

Keywords:
superpixel segmentation, clustering, vegetation regions, percentage composition.

Citation:
Varlamova AA, Denisova AY, Sergeev VV. Earth remote sensing data processing for obtaining vegetation types maps. Computer Optics 2018; 42(5): 864-876. DOI: 10.18287/2412-6179-2018-42-5-864-876.

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. Yu Q, Gong P, Clinton N, Biging G, Kelly M, Schirokauer D. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing 2006; 72(7): 799-811. DOI: 10.14358/PERS.72.7.799.
3. Sinyavskiy YN, Pestunov IA, Dubrovskaya OA, Rylov SA, Melnikov PV, Ermakov NB, Polyakova MA. Methods and technology for segmentation of images with high spatial resolution for studies of nature and man-made objects [In Russian]. Computational Technologies 2016; 21(1): 127-140.
4. Chang S-H, Westfield MJ, Lehmann F, Oertel D, Richter R. 79-channel airborne imaging spectrometer. Proc SPIE 1993; 1937: 164-173. DOI: 10.1117/12.157053.
5. Flanders D, Hall-Beyer M, Pereverzoff J. Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Canadian Journal of Remote Sensing 2014; 29(4): 441-452. DOI: 10.5589/m03-006.
6. Questier F, Put R, Coomans D, Walczak B, Vander Heyden Y. The use of CART and multivariate regression trees for supervised and unsupervised feature selection. Chemometrics and Intelligent Laboratory Systems 2005; 76(1): 45-54. DOI: 10.1016/j.chemolab.2004.09.003.
7. MacQueen JB. Some methods for classification and analysis of multivariate observations. Proc Fifth Symposium on Math, Statistics, and Probability 1967; 1: 281-297.
8. Mutanga O, Adam E, Cho MA. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation: 2012; 18: 399-406. DOI: 10.1016/j.jag.2012.03.012.
9. Borzov SM, Potaturkin OI. Vegetable cover type classification using hyperspectral remote sensing [In Russian]. Novosibirsk State University Journal of Information Technologies 2014; 12(4): 13-22.
10. Soifer VA, ed. Methods for computer image processing [In Russian]. Moscow: “Fizmatlit” Publisher; 2003. ISBN: 5-9221-0270-2.
11. Felzenszwalb PF, Huttenlocher DP. Efficient graph-based image segmentation. International Journal of Computer Vision 2004; 59(2): 167-181. DOI: 10.1023/B:VISI.0000022288.19776.77.
12. Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22(8): 888-905. DOI: 10.1109/34.868688.
13. Liu M-Y, Tuzel O, Ramalingam S, Chellappa R. Entropy Rate Superpixel Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011; 2097-2104. DOI: 10.1109/CVPR.2011.5995323.
14. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 2012; 34(11): 2274-2282. DOI: 10.1109/TPAMI.2012.120.
15. Li Z, Chen J. Superpixel segmentation using linear spectral clustering. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015; 1356-1363. DOI: 10.1109/CVPR.2015.7298741.
16. Wang J, Wang X. VCells: simple and efficient superpixels using Edge-Weighted Centroidal Voronoi Tessellations. IEEE Transactions on Pattern Analysis and Machine Intelligence 2012; 34(6): 1241-1247. DOI: 10.1109/TPAMI.2012.47.
17. Kavelenova LM, Prokhorova NV, Korchikov ES, Denisova AY, Terentyeva DA. Concerning the possibilities of successional changes revealing in anthropogenically transformed ecosystems on the base of remote sensing and ground-based survey data integration. CEUR Workshop Proceedings 2017; 1901: 99-103.
18. Anshakov GP, Raschupkin AV, Zhuravel YV. Hyperspectral and multispectral RESURS-P data fusion for increase of their informational content [In Russian]. Computer Optics 2015; 39(1): 77-82. DOI: 10.18287/0134-2452-2015-39-1-77-82.
19. Sergeyev GA, Yantush DA. Statistical methods of natural objects investigation [In Russian]. Leningrad: “Gidrometeoizdat” Publisher; 1973.
20. Ma J-Q. Content-based image retrieval with HSV color space and texture features. IEEE International Conference on Web Information Systems and Mining 2009; 61-63. DOI: 10.1109/WISM.2009.20.
21. Singh KK, Pal K, Nigam MJ. Shadow detection and removal from remote sensing images using NDI and morphological operators. International journal of computer applications 2012; 42(10): 37-40. DOI: 10.5120/5731-7805.

---

© 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