Skip to main content
SpringerLink
Log in

Research on auto-classification method of remote sensing images in mountainous areas—An application in Southwest of China

  • Published:
Geo-spatial Information Science

Abstract

In mountainous areas, it is the undulant terrain, various types of geomorphic and land use that make the remote sensing images great metamorphism. Moreover, due to the elevation, there are many areas covered with shadow, clouds and snow that make the images more inaccurate. As a result, it would be very difficult to carry out auto-classification of RS images in these areas. The study took Southwest China as the case study area and the TM images, SPOT images as the basic information sources assisted by the auxiliary data of DEM, NDVI, topographical maps and soil maps to preprocess the images. After preprocessing by topographic correction and wiping off clouds, snow and shadows, all the image data were stacked together to form the images to be classified. Then, the research used segmentation technology and hierarchical method to extract the main types of land use in the area automatically. The results indicated that the qualitative accuracies of all types of land use extracted in Southwest China were above 90 percent, and the quantitative accuracies was above 86 percent. The goal of reducing workloads had been realized.

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

Similar content being viewed by others

References

  1. Luo Jiancheng, Wang Qinmin, Ma Jianghong, et al. (2002) EM based maximumlikehood classifier for remotely sensed data[J]. Acta Geodaetica Cartographica Sinica, 31(3):234–239(in Chinese)

    Google Scholar 

  2. Liu Yalan, Yang Shouyi, Wang Tao, et al. (2002)A study on segmentation based classification approaches for remotely sensed imagery [J]. Journal of Remote Sensing, 6(5): 357–362 (in Chinese)

    Google Scholar 

  3. Li Ainong, Jiang Xiaobo, Ma Zezhong, et al. (2003) The application of RS automatic classification to land use survey in southwest China [J]. Remote Sensing Technology and Application, 18(5): 282–285(in Chinese)

    Google Scholar 

  4. Zhou Wancun, Sun Yuqiu(1987) Remote sensing analysis of the environmental capacity of land cover in three gorges reservoir area[M]. Beijing: Science Press

    Google Scholar 

  5. Fuller R M(1994) The land cover map of great britain: an automated classification of Landsat thematic mapper data. photogram metric [J]. Engineering and Remote Sensing, 60(5):553–562

    Google Scholar 

  6. Zhang Yan, Wang Tao, Zhao Liang, et al.(2006) A quick correcting method for Landsat SPOT images [J]. Science of Surveying and Mapping, 31(5): 71–73(in Chinese)

    Google Scholar 

  7. Dhruba P S(2001) Land use classification in mountainous areas: integration of image processing[J]. Digital Elevation Data and Field Knowledge, 3(1): 236–239

    Google Scholar 

  8. Zhang Baolei, Song Mengqiang, Zhou Wancun(2005) Exploration on method of auto-classification for main ground objects of Three Gorges Reservoir Area [J]. Chinese Geographical Science, 2005, 15(2): 157–161(in Chinese)

    Article  Google Scholar 

  9. Feng C C (2004) Flewelling computers, assessment of semantic similarity between land use/land cover classification systems [J]. Environment and Urban Systems, 28: 229–246

    Article  Google Scholar 

  10. Jia X P, John A R (1998) Progressive two-class decision classification for optimization of class discriminations [J]. Remote sensing of Enviroment, 63(3): 289–299

    Article  Google Scholar 

  11. Chen Xiaoling, Cai Xiaobin, Li Hui(2007) Expert classification method based on patch-based neighborhood searching algorithm[J]. Geo-spatial Information Science, 10(1): 37–43

    Article  Google Scholar 

  12. Shresthan D P, Zinck J A(2001) Land use classification in mountainous areas: integration of image processing-digital elevation data and field knowledge [J]. International Journal of Applied Earth Observation and Geo-information, 3(1): 78–85

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chinese Research Academy of Environmental Sciences, Anwai, No.8 Dayangfang, Beijing, 100012, China

    Chaoyang Feng

Authors
  1. Chaoyang Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shumin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Baolei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shihai Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jixi Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chaoyang Feng.

Additional information

Supported by the National Public Welfare Project on Environmental Protection (2007KYYW21), the Program of National Science and Technology research( 2006BAC01A01-05).

About this article

Cite this article

Feng, C., Zhang, S., Zhang, B. et al. Research on auto-classification method of remote sensing images in mountainous areas—An application in Southwest of China. Geo-spat. Inf. Sci. 12, 191–196 (2009). https://doi.org/10.1007/s11806-009-0037-z

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11806-009-0037-z

Keywords

CLC number

Search

Navigation