Chanida_Suwanprasit
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Transcript Chanida_Suwanprasit
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
2/20
Outline
Introduction
Objective
Methodology
Results
Conclusions
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
5/20
Objective
To examine effects of pixel size on
land use classification in Kathu district,
Phuket, Thailand
Study area: Kathu, Phuket
Kamala
Kathu
Patong
7/20
6/20
Data set specification
Imagery Source
LANDSAT 5 TM
Resolution (m)
Band
Spectral Type
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
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
THEOS
Landsat 5 Spectral Bands
Band 1 (Blue)
Band 4 (NIR)
10/20
Band 2 (Green)
Band 3 (Red)
Band 5 (NIR)
Band 7 (MIR)
THEOS Spectral Bands
Band 1 (Red)
Band 2 (Green)
Band 3 (Blue)
Band 4 (NIR)
11/20
9/20
True Color
THEOS
Landsat 5
RGB (4,3,2)
THEOS
13/20
Landsat 5
Process Overview
12/20
Data Set
THEOS
Control points
Training
area
Landsat 5
Unsupervised
K-Mean
Supervised
SVMs
Test area
Land use Classification Map
THEOS
LandSat 5
Classes
• Forest
• Built-up
• Road
• Water
• Agriculture
• Grassland
• Bare land
Unsupervised Classification:
K-Mean (7 Classes)
THEOS
14/20
Landsat 5
Support Vector Machines : SVMs
THEOS
Landsat
Forest
Bare land
Built - up
Grassland
Water
Road
16/20
Class Confusion Matrix
THEOS
Class
17/20
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)
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
20/20
Acknowledgement
Faculty of Technology and Environment
Prince of Songkla University, Phuket Campus
Geo-Informatics and Space Technology Development
Agency (Public Organization)
UniNet
Thank you for your kind attention