Development of a Neural Network based Algorithm for Multi-Scale Roughness Parameters and Soil Moisture Retrieval
The overall objective of this paper is to retrieve soil
surfaces parameters namely, roughness and soil moisture related to
the dielectric constant by inverting the radar backscattered signal
from natural soil surfaces.
Because the classical description of roughness using statistical
parameters like the correlation length doesn't lead to satisfactory
results to predict radar backscattering, we used a multi-scale
roughness description using the wavelet transform and the Mallat
algorithm. In this description, the surface is considered as a
superposition of a finite number of one-dimensional Gaussian
processes each having a spatial scale. A second step in this study
consisted in adapting a direct model simulating radar backscattering
namely the small perturbation model to this multi-scale surface
description. We investigated the impact of this description on radar
backscattering through a sensitivity analysis of backscattering
coefficient to the multi-scale roughness parameters.
To perform the inversion of the small perturbation multi-scale
scattering model (MLS SPM) we used a multi-layer neural network
architecture trained by backpropagation learning rule. The inversion
leads to satisfactory results with a relative uncertainty of 8%.
[1] F. Mattia, and T. Le Toan, "Backscattering properties of multi-scale
rough surfaces". Journal of Electromagnetic Waves and Applications,
13: 493-528, 1999.
[2] F. Mattia, and T. Le Toan, " An analytical, numerical, and experimental
study of backscattering from multi-scale soil surfaces." Radio Science,
Volume 36, Number 1 : 119-135, 2001.
[3] L.E. Church,. "Fractal surface finish." Applied Optics, vol.27, n.8, 1998.
[4] M. Davidson, T. Le Toan, F. Mattia, G. Satalino, T. Manninen, and M.
Borgeaud,. "On the characterisation of agricultural soil roughness for
radar sensing studies." IEEE Trans. Geosc.Rem.Sens., 38: 630-640,
2000.
[5] C.A. Guerin, M. Holschneider, and M. Saillard, "Electromagnetic
scattering from multi-scale rough surfaces." Waves Random Media, 7:
331-349, 1997.
[6] I. Daubechies, "Ten lectures on Wavelet". CBMS-NFS Lecture Notes,
NR.61, SIAM, 1992.
[7] S.G. Mallat, "Theory of multi-resolution signal decomposition: The
Wavelet representation", IEEE Trans.Pattern analysis and machine
intelligence, vol II., 7, 1989.
[8] L. Bennaceur, Z. Belhadj, and M. R. Boussema. "A study of radar
backscattering multi-scale bi dimensional surface", The 2002 IEEE
International Geoscience and Remote Sensing Symposium and the 24 th
Canadian Symposium on Remote Sensing, Toronto, Canada, June 2002.
[9] A.K. Fung, Z. Li, and K.S. Chen, "Backscattering from a randomly
rough dielectric surface". IEEE Trans.Geosc.Rem.Sensing, 30 : 356-363,
1992.
[10] A.K. Fung, Microwave scattering and emission models and their
applications Artech House, 1994.
[11] R. M. Axline, and A.K. Fung, "Numerical computation from a perfectly
conducting random surface", IEEE, Trans.Antennas Propagat., 26 : 488-
582, 1978.
[12] T. K. Chan, Y. Kuga, A. Ishimaru, and C.T.C. Le, "Experimental studies
of bistatic scattering from two-dimensional conducting random rough
surfaces". IEEE Trans.On Geosc. And Remote sensing, Vol. 34, No.3,
1996.
[13] R.T Shin, Theory of Microwave Remote Sensing, John Wiley, New
York, 1985.
[14] B.B., Mandelbrot, and J.W. Van Ness,1968. "Fractional Brownian
motions, fractal noises and applications". Siam Rev., 10.
[15] T., Feder, Fractals, Plenum Press, 1988.
[16] G.W.,Wornell, " Wavelet-based representation for the 1/f family of
fractal process", Proc IEEE, vol. 81, October 1993.
[17] M., Dawson, A.K Fung. "A robust statistical based estimator for soil
moisutre retrieval from radar measurments. "
[18] L. Bennaceur, R. Bennaceur, Z. Belhadj, and M. R. Boussema, A
sensitivity analysis of radar backscattering coefficient to multi-scale
roughness description and radar parameters using the small perturbation
model. Proceedings of ICTTA 04, Damascus, Sirius, April 2004.
[1] F. Mattia, and T. Le Toan, "Backscattering properties of multi-scale
rough surfaces". Journal of Electromagnetic Waves and Applications,
13: 493-528, 1999.
[2] F. Mattia, and T. Le Toan, " An analytical, numerical, and experimental
study of backscattering from multi-scale soil surfaces." Radio Science,
Volume 36, Number 1 : 119-135, 2001.
[3] L.E. Church,. "Fractal surface finish." Applied Optics, vol.27, n.8, 1998.
[4] M. Davidson, T. Le Toan, F. Mattia, G. Satalino, T. Manninen, and M.
Borgeaud,. "On the characterisation of agricultural soil roughness for
radar sensing studies." IEEE Trans. Geosc.Rem.Sens., 38: 630-640,
2000.
[5] C.A. Guerin, M. Holschneider, and M. Saillard, "Electromagnetic
scattering from multi-scale rough surfaces." Waves Random Media, 7:
331-349, 1997.
[6] I. Daubechies, "Ten lectures on Wavelet". CBMS-NFS Lecture Notes,
NR.61, SIAM, 1992.
[7] S.G. Mallat, "Theory of multi-resolution signal decomposition: The
Wavelet representation", IEEE Trans.Pattern analysis and machine
intelligence, vol II., 7, 1989.
[8] L. Bennaceur, Z. Belhadj, and M. R. Boussema. "A study of radar
backscattering multi-scale bi dimensional surface", The 2002 IEEE
International Geoscience and Remote Sensing Symposium and the 24 th
Canadian Symposium on Remote Sensing, Toronto, Canada, June 2002.
[9] A.K. Fung, Z. Li, and K.S. Chen, "Backscattering from a randomly
rough dielectric surface". IEEE Trans.Geosc.Rem.Sensing, 30 : 356-363,
1992.
[10] A.K. Fung, Microwave scattering and emission models and their
applications Artech House, 1994.
[11] R. M. Axline, and A.K. Fung, "Numerical computation from a perfectly
conducting random surface", IEEE, Trans.Antennas Propagat., 26 : 488-
582, 1978.
[12] T. K. Chan, Y. Kuga, A. Ishimaru, and C.T.C. Le, "Experimental studies
of bistatic scattering from two-dimensional conducting random rough
surfaces". IEEE Trans.On Geosc. And Remote sensing, Vol. 34, No.3,
1996.
[13] R.T Shin, Theory of Microwave Remote Sensing, John Wiley, New
York, 1985.
[14] B.B., Mandelbrot, and J.W. Van Ness,1968. "Fractional Brownian
motions, fractal noises and applications". Siam Rev., 10.
[15] T., Feder, Fractals, Plenum Press, 1988.
[16] G.W.,Wornell, " Wavelet-based representation for the 1/f family of
fractal process", Proc IEEE, vol. 81, October 1993.
[17] M., Dawson, A.K Fung. "A robust statistical based estimator for soil
moisutre retrieval from radar measurments. "
[18] L. Bennaceur, R. Bennaceur, Z. Belhadj, and M. R. Boussema, A
sensitivity analysis of radar backscattering coefficient to multi-scale
roughness description and radar parameters using the small perturbation
model. Proceedings of ICTTA 04, Damascus, Sirius, April 2004.
@article{"International Journal of Information, Control and Computer Sciences:60196", author = "L. Bennaceur Farah and I. R. Farah and R. Bennaceur and Z. Belhadj and M. R. Boussema", title = "Development of a Neural Network based Algorithm for Multi-Scale Roughness Parameters and Soil Moisture Retrieval", abstract = "The overall objective of this paper is to retrieve soil
surfaces parameters namely, roughness and soil moisture related to
the dielectric constant by inverting the radar backscattered signal
from natural soil surfaces.
Because the classical description of roughness using statistical
parameters like the correlation length doesn't lead to satisfactory
results to predict radar backscattering, we used a multi-scale
roughness description using the wavelet transform and the Mallat
algorithm. In this description, the surface is considered as a
superposition of a finite number of one-dimensional Gaussian
processes each having a spatial scale. A second step in this study
consisted in adapting a direct model simulating radar backscattering
namely the small perturbation model to this multi-scale surface
description. We investigated the impact of this description on radar
backscattering through a sensitivity analysis of backscattering
coefficient to the multi-scale roughness parameters.
To perform the inversion of the small perturbation multi-scale
scattering model (MLS SPM) we used a multi-layer neural network
architecture trained by backpropagation learning rule. The inversion
leads to satisfactory results with a relative uncertainty of 8%.", keywords = "Remote sensing, rough surfaces, inverse problems,
SAR, radar scattering, Neural networks and Fractals.", volume = "2", number = "5", pages = "1661-6", }