ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 1, pp. 105-116

Some applications of remote sensing image segmentation

E.S. Ivanov 1 
1 Program Systems Institute RAS, Pereslavl-Zalessky, Russia

Accepted: 13.12.2015
DOI: 10.21046/2070-7401-2016-13-1-105-116

This paper is devoted to segmentation methods of remote sensing multispectral images. Examples of the most common applications involving image segmentation are given. Three basic algorithms of image segmentation are described: threshold segmentation, segmentation by building areas, and segmentation by border highlighting. The results of the algorithms, as well as their advantages and drawbacks, are demonstrated. Modern developing methods of image segmentation are also presented, their features, benefits and results are discussed. The paper describes channels of multispectral images and information one can derive by processing data from individual channels or their combinations. It is shown that using multispectral images instead of RGB ones is preferable in many important applications.
Keywords: image segmentation, remote sensing, computer vision, image processing, multispectral images
Full text


  1. Ahmetshina L.G., Udovik I.M., Fazovaya segmentatsiya multispektralnyih slabokontrastnyih izobrazheniy (Phase segmentation of low-contrast multispectral images), Iskusstvennyiy intellect, 2011, Vol. 3, pp. 200-206, available at:
  2. Distantsionnoe zondirovanie Zemli (Remote Sensing of the Earth), JSC Russian Space Systems, available at:
  3. Zhilenev M.Yu., Obzor primeneniya multispektralnyih dannyih DZZ i ih kombinatsiy pri tsifrovoy obrabotke (Review of the usage of multispectral remote sensing channels and their combination in digital processing), Geomatika, 2009, Vol. 3, pp. 56-64, available at:
  4. Multispektralnyie dannyie DZZ i interpretatsiya kombinatsiy kanalov pri tsifrovoy obrabotke (Channels of remote sensing multispectral images and interpretation of their combination in digital processing), MapExpert, available at:
  5. Tou J.T., Gonzalez R.C., Pattern Recognition Principles, Moscow: Mir, 1978, 412 p.
  6. Cherepanov A.S., Vegetatsionnie indeksy (Vegetation indicies), Geomatika, 2011, Vol. 2, pp. 98-102, available at:
  7. Gao B.C., NDWI – A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space, Remote sensing of environment, 1996, Vol. 58, No. 3. pp. 257-266.
  8. Fu G., Zhao H., Li C., Shi L., Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique, Remote Sens., 2013, Vol. 5, pp. 3259-3279, doi:10.3390/rs5073259, available at:
  9. Muthukrishnan R., Radha M., Edge detection techniques for image segmentation, International Journal of Computer Science & Information Technology (IJCSIT), 2011, Vol. 3, No. 6, pp. 259-267, available at:
  10. Yuan J., Wang D., Li R., Remote Sensing Image Segmentation by Combining Spectral and Texture Features, IEEE transactions on geoscience and remote sensing, 2014, Vol. 52, No. 1, available at: