INVERSION OF THREE LAYERS MULTI-SCALE SPM MODEL BASED ON NEURAL NETWORK TECHNIQUE FOR THE RETRIEVAL OF SOIL MULTI-SCALE ROUGHNESS AND MOISTURE PARAMETERS
In this paper, a multi-layered multi-scale backscattering model for a lossy medium and a neural network inversion procedure has been presented. <br><br> We have used a bi-dimensional multi-scale (2D MLS) roughness description where the surface is considered as a superposition of a finite...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-04-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/1201/2015/isprsarchives-XL-7-W3-1201-2015.pdf |
| Summary: | In this paper, a multi-layered multi-scale backscattering model for a lossy medium and a neural network inversion procedure has
been presented.
<br><br>
We have used a bi-dimensional multi-scale (2D MLS) roughness description where the surface is considered as a superposition of a
finite number of one-dimensional Gaussian processes each one having a spatial scale using the wavelet transform and the Mallat
algorithm to describe natural surface roughness.
<br><br>
An adapted three layers 2D MLS small perturbations (SPM) model has been used to describe radar backscattering response of semiarid
sub-surfaces. The total reflection coefficients of the natural soil are computed using the multilayer model, and volumetric
scattering is approximated by the internal reflections between layers. The original multi-scale SPM model includes only the surface
scattering of the natural bare soil, while the multilayer soil modified 2D MLS SPM model includes both the surface scattering and
the volumetric scattering within the soil. This multi-layered model has been used to calculate the total surface reflection coefficients
of a natural soil surface for both horizontal and vertical co-polarizations.<br><br>
A parametric analysis presents the dependence of the backscattering coefficient on multi scale roughness and soil.
The overall objective of this work 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.
<br><br>
To perform the inversion of the modified three layers 2D MLS SPM model, we used a multilayer neural network (NN) architecture
trained by a back-propagation learning rule. |
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| ISSN: | 1682-1750 2194-9034 |