DocumentCode :
872742
Title :
On neural network algorithms for retrieving forest biomass from SAR data
Author :
Del Frate, Fabio ; Solimini, Domenico
Author_Institution :
Dipt. di Informatica, Univ. of Rome "Tor Vergata", Italy
Volume :
42
Issue :
1
fYear :
2004
Firstpage :
24
Lastpage :
34
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.
Keywords :
data acquisition; forestry; geophysical techniques; neural nets; regression analysis; remote sensing by radar; synthetic aperture radar; vegetation mapping; L-band; P-band; SAR data; algorithm sensitivity; channel combinations; extended pruning procedure; forest biomass retrieval; information content analysis; linear regressions; measurement noise; model-based technique; multifrequency multipolarization backscattering; multifrequency multipolarization measurements; neural network algorithms; nonlinear regressions; polarization states; radar backscattering coefficients; radar measurements; soil parameters; speckle; synthetic aperture radar; vegetation; Algorithm design and analysis; Backscatter; Biomass; Frequency; Information retrieval; Neural networks; Noise measurement; Polarization; Radar; Soil measurements;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2003.817220
Filename :
1262582
Link To Document :
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