In this paper, an approach is proposed that predicts fully polarimetric data from dual polarimetric data, and then applies
selected supervised algorithm for dual polarimetric, pseudo-fully polarimetric and fully polarimetric dataset for the land
cover classification comparison. A regression model has been developed to predict the complex variables of VV
polarimetric component and amplitude independently using corresponding complex variables and amplitude in HH and
HV bands. Support vector machine (SVM)is implemented for the land cover classification. Coherency matrix and
amplitude were used for all dataset for the land cover classification independently.They are used to compare the data
from different perspective. Finally, a post processing technique is implemented to remove the isolated pixels appeared as
a noise. AVNIR-2 optical data over the same area is used as ground truth data to access the classification accuracy.The
result from SVM indicates that the fully polarimetric mode gives the maximum classification accuracy followed by
pseudo-fully polarimetric and dual polarimetric datasets using coherency matrix input for fully polarimetric image and
pseudo-fully polarimetric image and covariance matrix input for dual polarimetric image. Additionally, it is observed
that pseudo-fully polarimetric image with amplitude input does not show the significant improvement over dual
polarimetric image with same input.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.