Skip to main content

Advertisement

SpringerLink
Log in

Classification of Satellite Images in Assessing Urban Land Use Change Using Scale Optimization in Object-Oriented Processes (A Case Study: Ardabil City, Iran)

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

By using satellite imagery, the recognition and evaluation of various phenomena and extraction of information necessary for the planning of land resources or other purposes are easily accomplished. The purpose of this study is to compare the efficiency of seven commonly used methods of monitored classification of satellite data to evaluate land use changes using TM and OLI Landsat, IRS, Spot5 and Quick Bird bands as well as different color combinations of these images to detect agricultural land, residential areas and aquatic areas using object-oriented processing. Digital processing of satellite images was carried out in 1998 and 2016 using advanced methods. Training samples were extracted in five user classes by eCognition software using segmentation scale optimization, different color combinations and coefficients of shape and compression. The appropriate segmentation scale for arable land, human complications and the blue areas were, respectively, 50, 8 and 10. Then each image was classified separately using seven methods and extracted samples, and efficiency of each classification method was obtained by calculating two general health and Kappa coefficients. The results show that the accuracy of each classification method and the neural network with a total accuracy of 94.475 and Kappa coefficient of 92.095 were selected as the most accurate classification method. These results show that the sampling of educational samples with proper precision of the classes in the images and dependency probability of each satellite images pixel can be useful in classifying group available in helpful area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Baatz, M., & Schpe, A. (2000). Multiresolution segmentation—an optimization approach for high quality multi-scale image segmenta-tion. In J. Strobl, T. Blaschke, & G. Greisebener (Eds.), Angewandte Geographische Informationsverarbeitung XII. Beitra ¨ge zum AGIT- SymposiumSalzburg (Vol. 200, pp. 12–23). Karlsruhe7 Herbert Wichmann Verlag.

  • Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 10–21.

    Article  Google Scholar 

  • Chaudhuri, B., & Sarkar, N. (1995). Texture segmentation using fractal dimension. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(1), 72–77.

    Article  Google Scholar 

  • Diallo, Y., Hu, G., & Wen, X. (2009). Applications of remote sensing in land use/land cover change detection in puer and simao counties, Yunnan province. Journal of American Science, 5(4), 157–166.

    Google Scholar 

  • Drăguţ, L., & Eisank, C. (2010). Automated classification of topography from SRTM data using object-based image analysis. Geomorphology, 141–142, 21–33.

    Google Scholar 

  • Friedl, M. A., et al. (2002). Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 83, 287–302.

    Article  Google Scholar 

  • Herold, M., Guenther, S., & Clarke, K. (2003). Mapping urban areas in the Santa Barbara South coast using IKONOS data and eCognition. In eCognition application note, (vol 4, issue 1, p. 2). Munich: Definiens Imaging GmbH.

  • Hodgson, M. E., Jensen, J. R., Tullis, J. A., Riordan, K. D., & Archer, C. M. (2003). Synergistic use of LIDAR and color aerial photography for mapping urban parcel imperviousness. Photogrammetric Engineering & Remote Sensing, 69(9), 973–980.

    Article  Google Scholar 

  • Hofmann, T., Puzicha, J., & Buhmann, J. (1998). Unsupervised texture segmentation in a deterministic annealing framework. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 803–818.

    Article  Google Scholar 

  • Jensen, J. R. (2014). Remote sensing of the environment an earth resource perspective (2nd ed., p. 619). Pearson Education Limited.

  • Lamchin, M., et al. (2016). Assessment of land cover change and desertification using remote sensing technology in a local region of Mongolia. Advances in Space Research, 57(1), 64–77.

    Article  Google Scholar 

  • Ling, F., Li, W., Du, Y., & Li, X. (2010). Land cover change mapping at the subpixel scale with different spatial-resolution remotely sensed imagery. IEEE Geoscience and Remote Sensing Letters, 8, 182–186.

    Article  Google Scholar 

  • Liu, T., & Yang, X. (2015). Monitoring land changes in an urban area using satellite imagery, GIS and landscape metrics. Original Research Article Applied Geography, 56, 42–54.

    Article  Google Scholar 

  • Makrouni, S., Sabzghabaei, Gh.R, Yousefi Khaneghah, Sh., & Soltanian, S. (2016). Detection of 10. land use changes in Hoor Al Azim wetland using remote sensing and geographic information system techniques. Journal of RS and GIS for Natural Resources, 7(3), 89–99.

    Google Scholar 

  • Man, Q., Dong, P., & Guo, H. (2015). Pixel- and feature-level fusion of hyperspectral and LiDAR data for urban land-use classification. International Journal of Remote Sensing, 36(6), 1618–1644.

    Article  Google Scholar 

  • Metelka, V., Baratoux, L., Jessell, M.W., Barth, A., Ježek, J., & Naba, S. (2018). Automated regolith landform mapping using airborne geophysics and remote sensing data, Burkina Faso, West Africa. Remote Sensing of Environment. 204, 964–978

    Google Scholar 

  • Mirza’i Zadeh, M., Nicenejad, M., & Kadaikli's Oladi, J. (2015). Evaluation of non-parametric supervised classification algorithms in the preparation of land cover maps using Landsat images. Remote Sensing and Geographic Information System in Natural Resources, 29-44 (3), 6

    Google Scholar 

  • Nazmfar, H. (2012). An analysis of urban system with emphasis on entropy model (Case Study: the cities of East Azerbaijan Province). Indian Journal of Science and Technology, 5(9), 3340–3344.

    Google Scholar 

  • Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geo information, 12, S27–S31.

    Article  Google Scholar 

  • Pohl, C., & van Genderen, J. (2017). Remote sensing image fusion a practical guide. Boca Raton: CRC Press Taylor & Francis Group.

    Google Scholar 

  • Pons, X. (2003). Post-classification change detection with data from different sensors: Some accuracy considerations. International Journal of Remote Sensing, 24, 4975–4976.

    Article  Google Scholar 

  • Richards J., A. (2013). Remote sensing digital image analysis. (5th ed., pp. 494). Springer.

  • Şatır, O., & Berberoğlu, S. (2012). Land use/cover classification techniques using optical remotely sensed data in landscape planning. In Ozyavuz, S. (Ed.) Landscape Planning, InTech. ISBN:978-953-51-0654-8

  • Singh, R., & Gupta, R. (2016). Improvement of classification accuracy using image fusion techniques. In 2016 International Conference on Computational Intelligence and Applications (ICCIA), (pp. 36–40). Jeju. https://doi.org/10.1109/ICCIA.2016.21.

  • Statistical Yearbook. (2011). Population and housing census. Ardabil Province: Statistical Yearbook.

    Google Scholar 

  • Topaloğlu, R. H., Sertel, E., & Musaoğlu, N. (2016). Assessment of classification accuracies of sentinel-2 and Landsat-8 data for land cover/use mapping. In The International archives of the photogrammetry, remote sensing and spatial information sciences, volume XLI-B8, 2016 XXIII ISPRS Congress, 12–19 July 2016. Prague, Czech Republic.

  • Tullis, J. A., & Jensen, J. R. (2003). Expert system house detection in high spatial resolution imagery using size, shape, and context. Geocarto International, 18(1), 5–15.

    Article  Google Scholar 

  • Zhan, X., Sohlberg, R. A., Townshend, J. R. G., DiMiceli, C., Carrol, M. L., Eastman, J. C., Hansen, M. C. & De- Fries, R. S (2002). Detection of land cover changes using MODIS 250 m data. Remote Sensing of Environment, 83, 336–350.

    Article  Google Scholar 

  • Zhang, H., Lin, H., & Li, Y. (2015). Impacts of feature normalization on optical and SAR data fusion for land use/land cover classification. IEEE Geoscience and Remote Sensing Letters, 12(5), 1061–1065.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Geography and Urban Planning, University of Mohaghegh Ardabili, Ardabili, Iran

    Hossein Nazmfar

  2. Remote Sensing, University of Tabriz, Tabriz, Iran

    Jafar Jafarzadeh

Authors
  1. Hossein Nazmfar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jafar Jafarzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hossein Nazmfar.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nazmfar, H., Jafarzadeh, J. Classification of Satellite Images in Assessing Urban Land Use Change Using Scale Optimization in Object-Oriented Processes (A Case Study: Ardabil City, Iran). J Indian Soc Remote Sens 46, 1983–1990 (2018). https://doi.org/10.1007/s12524-018-0850-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-018-0850-7

Keywords

Search

Navigation