Towards Complex-Valued Neural Algorithms for Forest Parameters Estimation from Polinsar Data

Author

Angiuli, E. ; Del Frate, F. ; Polsinelli, B. ; Solimini, D.

Author_Institution

Dept. of Comput., Univ. Tor Vergata, Rome

Volume

2

fYear

2008

fDate

7-11 July 2008

Abstract

We discuss the development and application of a Complex-Valued Neural Network (CVNN) algorithm for retrieving forest biomass from polarimetric interferometric SAR data. After discussing some features of the net and of the training procedures, we analyze the performance of the algorithm in inverting combinations of simulated radar backscattering at different polarization states. The CVNN performance is compared with that of other retrieval algorithms.

Keywords

environmental factors; forestry; geophysical signal processing; geophysical techniques; neural nets; parameter estimation; radar interferometry; radar polarimetry; radar signal processing; remote sensing by radar; synthetic aperture radar; vegetation mapping; CVNN algorithm; PolInSAR data; complex valued neural algorithms; complex valued neural network; forest biomass retrieval; forest parameter estimation; neural net features; neural net training; polarimetric interferometric SAR data; radar backscattering data; Algorithm design and analysis; Analytical models; Backscatter; Biomass; Information retrieval; Neural networks; Parameter estimation; Performance analysis; Polarization; Radar polarimetry; Forest biomass; complex valued neural networks; polarimetric interferometric synthetic aperture radar (PolInSAR);

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International

Conference_Location

Boston, MA

Print_ISBN

978-1-4244-2807-6

Electronic_ISBN

978-1-4244-2808-3

Type

conf

DOI

10.1109/IGARSS.2008.4779074

Filename

4779074