DocumentCode :
1523053
Title :
Comparison of Vector Stacking, Multi-SVMs Fuzzy Output, and Multi-SVMs Voting Methods for Multiscale VHR Urban Mapping
Author :
Huang, Xin ; Zhang, Liangpei
Author_Institution :
State Key Lab. of Inf. Eng. in Surveying, Mapping & Remote Sensing, Wuhan Univ., Wuhan, China
Volume :
7
Issue :
2
fYear :
2010
fDate :
4/1/2010 12:00:00 AM
Firstpage :
261
Lastpage :
265
Abstract :
The objective of this letter is to integrate multiscale information for urban mapping using very high resolution (VHR) imagery. Three multiscale fusion methods were presented: 1) vector stacking (VS); 2) multiple support vector machines (multi-SVMs) fuzzy output; and 3) multi-SVMs voting. Two kinds of spatial features were used to obtain multiscale representations of VHR images: morphological structural features and object-based approaches. In experiments, the Reflective Optics System Imaging Spectrometer-03 Pavia Center and University, the Hyperspectral Digital Imagery Collection Experiment Washington DC Mall, and the Quickbird Beijing data sets were used for algorithm validation. The experimental results revealed that, in most cases, the VS fusion outperformed other methods because it was able to create a new high-dimensional multiscale feature space and enhance the class separability. It was also shown that the multi-SVMs fuzzy fusion could optimize and reorganize the multiscale information effectively. Furthermore, multi-SVMs fuzzy output was better than multi-SVMs voting because the former was able to exploit the probabilistic output, while the latter only considered the crisp classification label. In addition, it is suggested that VS fusion is suitable for morphological features; however, for the object-based classification, the multiscale fusion methods do not necessarily yield better results than the single-scale classification in terms of accuracies.
Keywords :
geophysical image processing; geophysical techniques; support vector machines; Hyperspectral Digital Imagery Collection Experiment; Quickbird Beijing data sets; Reflective Optics System Imaging Spectrometer-03 Pavia Center and University; VHR images; VS fusion; Washington DC Mall; crisp classification label; morphological structural features; multiSVM fuzzy output; multiSVM voting; multiple support vector machines fuzzy output; multiscale VHR urban mapping; multiscale fusion methods; object-based classification; vector stacking; very high resolution imagery; Fusion; high resolution; morphological; multiscale; object-based classification; support vector machines (SVMs);
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2009.2032563
Filename :
5299026
Link To Document :
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