| dc.description.abstract | There are two main topics will discuss in this paper, pixel-level image
fusion based on Principle Component Analysis (PCA), and feature-level
image fusion based on Dempster-Shafer evidence theory.
In pixel-level case, the SAR image at HV polarization is relatively
sensitive to the vegetation canopy. We combined the HV polarization
information from SAR and spectral characteristic from SPOT images in
an effort to enhance land cover classification. Before the fusion process,
wavelet transform was first applied to denoise the SAR image which
suffers from speckle contamination due to coherent process. The principle
component analysis (PCA) is used to fuse the SPOT and SAR images. In
so doing, the PC-1 component is replaced by SAR image (approximation
image, after wavelet transform) and then the inverse transform is
followed. At last, the maximum likelihood classifier was used for both
SPOT-XS images and fusion images.
In feature-level case, fully polarization information from SAR is used
to combine with spectral characteristic from SPOT images, mainly to
enhance land cover classification as well. We first denoise the SAR image
by Lee filter. Next, the maximum likelihood classifier based on different
distribution was used for SAR and SPOT images ( Based on Wishart
distribution and multivariate Gaussian distribution respectively), to
extract the conditional probability of each pixel for each class.
Dempster-Shafer evidence theory is then applied, to combine the
classified results of SAR and SPOT data.
Experimental results show that the classification accuracy is
dramatically improved by making use of the proposed methods above.
Data fusion can take advantage of the use of complementary information
to obtain a better overall accuracy than using single data source only. | en_US |