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Impacts of spatial resolution on land cover classification

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Impacts of spatial resolution on land cover classification. Chanida Suwanprasit and Naiyana Srichai Prince of Songkla University Phuket Campus. APAN 33 rd Meeting 13-17 February 2012. 2/20. Outline. Introduction Objective Methodology Results Conclusions. 3/20.

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Impacts of spatial resolution on land cover classification

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  1. Impacts of spatial resolution on land cover classification ChanidaSuwanprasit and NaiyanaSrichai Prince of Songkla University Phuket Campus APAN 33rdMeeting 13-17 February 2012

  2. 2/20 Outline • Introduction • Objective • Methodology • Results • Conclusions

  3. 3/20 Spatial Resolution is a measurement of the spatial detail in an image, which is a function of the design of the sensor and its operating altitude above the Earth’s surface (Smith, 2012). Classification Factors • Number of mixed Pixel • Number of ROIs • Scale or spatial resolution • Spectral resolution • Temporal resolution

  4. 5/20 Objective • To examine effects of pixel size on land use classification in Kathu district, Phuket, Thailand

  5. 7/20 Study area: Kathu, Phuket Kamala Kathu Patong

  6. 6/20 Data set specification

  7. 10/20 Band 1 (Blue) Band 2 (Green) Band 3 (Red) Landsat 5 Spectral Bands Band 4 (NIR) Band 7 (MIR) Band 5 (NIR)

  8. 11/20 Band 1 (Red) Band 2 (Green) THEOS Spectral Bands Band 3 (Blue) Band 4 (NIR)

  9. 9/20 True Color Landsat 5 THEOS

  10. 13/20 RGB (4,3,2) Landsat 5 THEOS

  11. Data Set 12/20 THEOS Landsat 5 Process Overview Unsupervised K-Mean • Classes • Forest • Built-up • Road • Water • Agriculture • Grassland • Bare land Control points Training area Supervised SVMs Test area Land use Classification Map THEOS LandSat 5

  12. 14/20 Landsat 5 THEOS Unsupervised Classification:K-Mean (7 Classes)

  13. 16/20 Landsat THEOS Support Vector Machines : SVMs Forest Built - up Bare land Grassland Road Water

  14. 17/20 Class Confusion Matrix

  15. 18/20 Conclusion • THEOS gave a higher classification accuracy than Landsat 5 for identifying land use in this study. • More Spectral bands from Landsat 5 with 30m is not appropriated for selecting clearly ROIs than THEOS with 15m resolution. • The better resolution image greatly reduce the mixed-pixel problem, and there is the potential to extract much more detailed information on land-use/land cover structures.

  16. 19/20 References • Duveiller, G. and P. Defourny (2010). "A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing." Remote Sensing of Environment114(11): 2637-2650. • Randall B. Smith (2012). "Introduction to Remote Sensing Environment (RSE)". Website: http://www.microimages.com.

  17. 20/20 Acknowledgement • Faculty of Technology and Environment Prince of SongklaUniversity, Phuket Campus • Geo-Informatics and Space Technology Development Agency (Public Organization) • UniNet

  18. Thank you for your kind attention

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