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First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)
A SVM-Based Change Detection Method from Bi-Temporal Remote Sensing Images in Forest Area
Adelaide, Australia
January 23-January 24
ISBN: 0-7695-3090-7
ASCII Text
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Dengkui Mo, Hui Lin, Jiping Li, Hua Sun, Zhuo Zhang, Yujiu Xiong, "A SVM-Based Change Detection Method from Bi-Temporal Remote Sensing Images in Forest Area," International Workshop on Knowledge Discovery and Data Mining, pp. 209-212, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008.
 
 
BibTex
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@article{ 10.1109/WKDD.2008.49,
author = {Dengkui Mo and Hui Lin and Jiping Li and Hua Sun and Zhuo Zhang and Yujiu Xiong},
title = {A SVM-Based Change Detection Method from Bi-Temporal Remote Sensing Images in Forest Area},
journal ={International Workshop on Knowledge Discovery and Data Mining},
volume = {0},
year = {2008},
isbn = {0-7695-3090-7},
pages = {209-212},
doi = {http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.49},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - International Workshop on Knowledge Discovery and Data Mining
TI - A SVM-Based Change Detection Method from Bi-Temporal Remote Sensing Images in Forest Area
SN - 0-7695-3090-7
SP209
EP212
A1 - Dengkui Mo,
A1 - Hui Lin,
A1 - Jiping Li,
A1 - Hua Sun,
A1 - Zhuo Zhang,
A1 - Yujiu Xiong,
PY - 2008
VL - 0
JA - International Workshop on Knowledge Discovery and Data Mining
ER -
 
The reliability of support vector machines for classifying multi-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection in forest regions. Firstly, multidate remote sensing images are co-registered and we have stacked the NDVI index layers of two dates in red, green, blue bands composite to perform a supervised classification. Secondly, sample pixels were manually selected from changed and unchanged area to be used in the training stage. Thirdly, for each pixel SVM produces a single output through its decision function, high detection overall accuracy (>96%) and overall Kappa coefficient (>0.89) were achieved using two Landsat images covering an 8-years period in study area. Lastly, SVM-based change detection with different kernel functions was compared using statistical evaluations. Keywords: change detection, support vector machines, classification, forest, remote sensing.
Citation:
Dengkui Mo, Hui Lin, Jiping Li, Hua Sun, Zhuo Zhang, Yujiu Xiong, "A SVM-Based Change Detection Method from Bi-Temporal Remote Sensing Images in Forest Area," wkdd, pp.209-212, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008
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