Geodesic
Multispectral images segmentation algorithm
Matematičeskoe modelirovanie, Tome 36 (2024) no. 1, pp. 25-40.

Voir la notice de l'article provenant de la source Math-Net.Ru

Multispectral images segmentation algorithm is presented. First, preliminary estimation of segments number is obtained. Second, image is decomposed into segments by non-iterative version of the k-means method. Third, statistical analysis is carried out; couples of segments which are realizations of the same random vector are found and merged. The results of testing algorithm on model and real (sensor HYPERION) data are presented. Real images segmentations where segments correspond to the different elements of the landscape are given.
Mots-clés : data segmentation, multispectral data. 
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     author = {O. V. Nikolaeva},
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O. V. Nikolaeva. Multispectral images segmentation algorithm. Matematičeskoe modelirovanie, Tome 36 (2024) no. 1, pp. 25-40. http://geodesic.mathdoc.fr/item/MM_2024_36_1_a2/

[1] J. Ke, Y. Guo, A. Sowmya, “A fast approximate unmixing algorithm based on segmentation”, Proc. of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshop, 2017, 66–72 | DOI | Zbl

[2] A. M. Belov, A. Iu. Denisova, “Algoritm vyiavleniia sluchainykh iskazhenii v sostave stseny na serii raznovremennykh izobrazhenii DZZ odnoi i toi zhe territorii”, Kompiuternaia optika, 43:5 (2019), 869–885 | DOI

[3] A. A. Varlamova, A. Iu. Denisova, V. V. Sergeev, “Informatsionnaia tekhnologiia obrabotki dannykh DZZ dlia otsenki arealov rastenii”, Komp. optika, 42:5 (2018), 864–876 | DOI

[4] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk, “SLIC superpixels compared to state-of-the-art superpixel methods”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 346:11 (2012), 2274–2282 | DOI

[5] V. A. Gorbachev, I. A. Krivorotov, A. O. Markelov, E. V. Kotliarova, “Semanticheskaia segmentatsiia sputnikovykh snimkov aeroportov s pomoshchiu svertochnykh neironnykh setei”, Kompiuternaia optika, 44:4 (2020), 636–645 | DOI

[6] E. S. Ivanov, I. P. Tishchenko, A. N. Vinogradov, “Segmentatsiia multispektralnykh snimkov s primeneniem svertochnykh neironnykh setei”, Sovremennye problemy DZZ iz kosmosa, 16:1 (2019), 25–34 | DOI

[7] R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan, “Automatic subspace clustering of high dimensional data for data mining applications”, Proc. ACM SIGMOD International Conference on Management Data, ACM Press, USA, 1998, 94–105 | DOI

[8] H. Nagesh, S. Goi, A. Choudhary, “Adaptive grids for clustering massive data sets”, Proc. of 1st SIAM International Conference on Data Mining, Chicago, USA, 2001, 1–17

[9] N. P. Lin, C. I. Chang, N. Y. Jan, H. J. Chen, W. H. Hao, “A deflected grid-based algorithm for clustering analysis”, Int. J. Math. Models Methods Appl. Sci., 1:1 (2007), 33–39

[10] I. A. Pestunov, S. A. Rylov, V. B. Berikov, “Ierarkhicheskie algoritmy klasterizatsii dlia segmentatsii multispektralnykh izobrazhenii”, Avtometriia, 51:4 (2015), 12–21

[11] A. Vedaldi, S. Soatto, “Quick shift and kernels methods for mode seeking”, Proc. of Conference Computer Vision, ECCV 2008, 705–718 | DOI

[12] A. Ibrahim, M. Salem, H.A Ali, “Automatic quick-shift segmentation for color images”, JCSI International Journal of Computer Science Iss., 11:3 (2014), 122–127

[13] I. A. Pestunov, V. B. Berikov, Iu. N. Siniavskii, “Segmentatsiia mnogospektralnykh izobrazhenii na osnove ansamblia neparametricheskikh algoritmov klasterizatsii”, Vestnik Sib. gos. aerokosmich. universiteta im. akad. M.F.Reshetneva, 2010, no. 5, 56–64

[14] S. V. Belim, P. E. Kutlunin, “Vydelenie konturov na izobrazheniiakh s pomoshchiu algoritma klasterizatsii”, Kompiuternaia optika, 39:1 (2015), 119–125 | DOI

[15] Y. Tarabalka, J. Chanussot, J. A. Benediktsson, “Segmentation and classification of hyperspectral images using watershed transformation”, Pattern Recognition, 43 (2010), 2367–2379 | DOI | Zbl

[16] O. V. Nikolaeva, “Statistical technique in clustering problems”, Math. Models Computer Simulations, 15:3 (2023), 445–453 | DOI | DOI

[17] E. V. Myasnikov, “Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches”, Computer Optics, 41:4 (2017), 564–572 | DOI

[18] B. Bahmani, B. Moseley, A. Vattani, R. Kumar, S. Vassilvitskii, “Scalable K-means++”, Proc. of the VLDB Endowment, 5:7 (2012), 622–633 | DOI | MR