Abstract
Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions. In most applications, GMM algorithm is directly applied on raw SAR data, its disadvantage is that forest and urban areas are classified with the same label and gives problems in interpretation. In this paper, a combination between the improved Freeman decomposition and GMM classification is proposed. The improved Freeman decomposition powers are used as feature vectors for GMM classification. The E-SAR polarimetric image acquired over Oberpfaffenhofen in Germany is used as data set. The result shows that the proposed combination can solve the standard GMM classification problem.
Highlights
A combination between improved Freeman decomposition and Gaussian mixture model (GMM) is proposed in this paper and compared with the standard GMM classification
The standard GMM consists in the use the scattering matrix components Shh, Shv and Svv as feature vector, the image is vectorized into D x N matrix where N is the number of pixels
Standard GMM classification fails to distinguish between forest and urban areas
Summary
SAR data processing is inevitable step to interpret different existing patterns or scattering mecanisms on polarimetric images. The algorithm models polarimetric coherency matrix as the sum of three different scattering mechanisms: surface, double bounce and volume scattering. The deorientation matrix is added in order to distinguish between the volume and the double bounce with orientation angle scatterings; there is no negative powers: Ps, Pd and Pv. The Gaussian mixture model (GMM) is an unsupervised classification method. A combination between improved Freeman decomposition and GMM is proposed in this paper and compared with the standard GMM classification. A polarimetric SAR system measures the backscattering coefficients using the transmitted and backscattered signals by antenna and scene under illumination respectively in different polarizations. If the radar is reciprocal, the cross-polarizations are considered as equal, given in (2)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
