On Neural Network Algorithms
for Retrieving Forest Biomass from SAR Data

Fabio Del Frate, and Domenico Solimini

Abstract

We discuss the application of neural network algorithms (NNAs) for retrieving forest biomass from multifrequency (L- and P-band) multipolarization (hh, vv, and vv) backscattering.

After discussing the training and pruning procedures, we examine the performances of neural algorithms in inverting combinations of radar backscattering coefficients at different frequencies and polarization states. The analysis includes an evaluation of the expected sensitivity of the algorithm to measurement noise stemming both from speckle and from fluctuations of vegetation and soil parameters. The NNA accomplishments are compared with those of
linear regressions for the same channel combinations.

The application of NNAs to invert actual multifrequency multipolarization measurements reported in literature is then considered. The NNA retrieval accuracy is now compared with those yielded by linear and nonlinear regressions and by a model-based technique. A direct analysis of the information content of the radar measurements
is finally carried out through an extended pruning procedure of the net.

Index Terms---Forest biomass, neural networks, synthetic aperture radar (SAR)

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