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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. - PowerPoint PPT Presentation
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Impacts of spatial resolution on land cover classification
Chanida Suwanprasit and Naiyana Srichai
Prince of Songkla University Phuket Campus
APAN 33rd Meeting 13-17 February 2012
Outline Introduction Objective Methodology Results Conclusions
2/20
Spatial Resolutionis 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
Objective To examine effects of pixel size on
land use classification in Kathu district,
Phuket, Thailand
5/20
Study area: Kathu, Phuket 7/20
Kathu
Kamala
Patong
Imagery Source Resolution (m) Band Spectral Type
LANDSAT 5 TM
30 1 (Blue) 0.45 – 0.52 m
30 2 (Green) 0.52 – 0.60 m
30 3(Red) 0.63 – 0.69 m
30 4 (NIR) 0.78 – 0.90 m
30 5 (NIR) 1.55 – 1.75 m
60 6 (TIR) 10.40 – 12.5 m
30 7(MIR) 2.80 – 2.35 m
THEOS
15 1 (Blue) 0.45 -0.52 m
15 2 (Green) 0.53 – 0.60 m
15 3 (Red) 0.62 – 0.69 m
15 4 (NIR) 0.77 – 0.90 m
Data set specification6/20
Band 1 (Blue) Band 2 (Green) Band 3 (Red)
Band 4 (NIR) Band 5 (NIR) Band 7 (MIR)
Landsat 5 Spectral Bands10/20
Band 1 (Red) Band 2 (Green)
Band 3 (Blue) Band 4 (NIR)
THEOS Spectral Bands11/20
True Color
THEOS Landsat 5
9/20
RGB (4,3,2)
THEOS Landsat 5
13/20
Process Overview
THEOS Landsat 5
Classes• Forest• Built-up• Road• Water• Agriculture• Grassland• Bare land
UnsupervisedK-Mean
SupervisedSVMs
Training area
Test area
Control points
THEOS LandSat 5
Land use Classification Map
Data Set
12/20
THEOS Landsat 5
Unsupervised Classification:K-Mean (7 Classes) 14/20
Support Vector Machines : SVMs
THEOS Landsat
Forest
Grassland
Bare land
Water
Built - up
Road
16/20
Class Confusion Matrix
Class
THEOS Landsat-5
Prod. Acc. (%)
User Acc. (%)
Prod. Acc. (%)
User Acc. (%)
Forest 97.47 96.81 100.00 100.00
Built-up 62.37 71.18 97.02 97.57
Road 74.89 64.62 90.15 90.59
Water 99.87 99.29 83.25 78.71
Bare land 76.78 91.31 60.88 66.78
Grassland 89.49 95.23 96.02 91.85
Agriculture 92.21 84.22 76.69 75.37
Overall Accuracy 90.65% (Kappa Co.= 0.88) 89.00% (Kappa Co.=0.87)
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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
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): 2637-2650.
Randall B. Smith (2012). "Introduction to Remote Sensing Environment (RSE)". Website: http://www.microimages.com.
19/20
Acknowledgement Faculty of Technology and Environment
Prince of Songkla University, Phuket Campus
Geo-Informatics and Space Technology Development Agency (Public Organization)
UniNet
20/20
Thank you for your kind attention
