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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.

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Presentation on theme: "Impacts of spatial resolution on land cover classification Chanida Suwanprasit and Naiyana Srichai Prince of Songkla University Phuket Campus APAN 33 rd."— Presentation transcript:

1 Impacts of spatial resolution on land cover classification Chanida Suwanprasit and Naiyana Srichai Prince of Songkla University Phuket Campus APAN 33 rd Meeting February 2012

2 Outline  Introduction  Objective  Methodology  Results  Conclusions 2/20

3 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). 3/20 Classification Factors  Number of mixed Pixel  Number of ROIs  Scale or spatial resolution  Spectral resolution  Temporal resolution

4 Spectral reflectance characteristics Source: Smith, /20

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

6 Study area: Kathu, Phuket 7/20 Kathu Kamala Patong

7 Imagery SourceResolution (m)BandSpectral Type LANDSAT 5 TM 301 (Blue) 0.45 – 0.52  m 302 (Green) 0.52 – 0.60  m 303(Red) 0.63 – 0.69  m 304 (NIR) 0.78 – 0.90  m 305 (NIR) 1.55 – 1.75  m 606 (TIR) – 12.5  m 307(MIR) 2.80 – 2.35  m THEOS 15 1 (Blue)  m 15 2 (Green) 0.53 – 0.60  m 15 3 (Red) 0.62 – 0.69  m 154 (NIR)0.77 – 0.90  m Data set specification 6/20

8 Source: Kathu, Phuket 8/20

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

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

11 True Color THEOS Landsat 5 9/20

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

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

14 Unsupervised Classification: K-Mean (7 Classes) 14/20

15 ROIs Separability Test Class ForestBuilt-upRoadWaterAgricultureGrasslandBare land Forest Built-up Road Water Agriculture Grassland Bare land THEOS’s ROIs Landsat’s ROIs 15/20

16 Support Vector Machines : SVMs THEOSLandsat Forest Grassland Bare land Water Built - up Road 16/20

17 Class Confusion Matrix Class THEOSLandsat-5 Prod. Acc. (%) User Acc. (%) Prod. Acc. (%) User Acc. (%) Forest Built-up Road Water Bare land Grassland Agriculture Overall Accuracy90.65% (Kappa Co.= 0.88)89.00% (Kappa Co.=0.87) 17/20

18 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. 18/20

19 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 Environment 114(11):  Randall B. Smith (2012). "Introduction to Remote Sensing Environment (RSE)". Website: 19/20

20 Acknowledgement  Faculty of Technology and Environment Prince of Songkla University, Phuket Campus  Geo-Informatics and Space Technology Development Agency (Public Organization)  UniNet 20/20

21 Thank you for your kind attention


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