A spectral analysis of multiple scattering effects In close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment

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A spectral analysis of multiple scattering effects In close range hyperspectral imagery of vegetation scenes:

application to nitrogen content assessment

Nathalie Al Makdessi, Gilles Rabatel, Martin Ecarnot, Pierre Roumet

To cite this version:

Nathalie Al Makdessi, Gilles Rabatel, Martin Ecarnot, Pierre Roumet. A spectral analysis of multiple scattering effects In close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment. 18. International Conference on Near Infrared Spectroscopy (ICNIRS 2017), Jun 2017, Copenhague, Denmark. 2017. �hal-02790781�

To analyze multiple scattering effects in the context of

hyperspectral imaging for plant phenotyping purposes

and to propose chemometrical methods to overcome

them

.

Close range hyperspectral imagery is a promising tool

for phenotyping or crop monitoring. In association with

Partial Least Square Regression, it allows to build high

spatial resolution maps of chemical content at the

canopy level.

Several optical phenomena must be taken into account

when applying this approach to vegetation scenes in

natural conditions:

Additive factors due to specular reflection

Multiplicative factors due to leaf inclination

Multiple Scattering Phenomena

Multiple scattering is produced when a leaf is

illuminated partly by direct light from the sky, and partly

by light reflection or transmission from neighboring

leaves

 Strong non-linear effects on its reflectance spectrum are produced

 Possible bias in regression results.

Removing Multiple Scattering by Oblique projection :

An oblique projection matrix following the W direction on B is applied on every spectra.

The two subspaces W and B are generated respectively by the scattering effect and the variable of interest (LNC: Leaf Nitrogen Content) in the spectral space.

Determination of B:

B is determined as the total inertia matrix of the loadings obtained by the calibration of the PLS-R on the basic spectra.

Determination of W:

Physical interpretation and formalization of multiple scattering

W is calculated by combining additive terms R_iu_T

iv generated by

the multiple scattering.

Some parameters must be adjusted: Maximum values of u and v, Maximum dimension of W, etc.  Various combinations of parameters were tested and the best combination was used.

W axis

B axis

S

PWB(S)

Oblique projection of a spectrum S

LNC prediction plots on simulated spectra (%DM).

Left: standard PLS-R model. Right: PLS-R model after oblique projection

Evaluation of the method in real conditions:

Tests on real plants

According to ASD measurements, a LNC range from about 2 to

3.5 % of dry matter (DM) was obtained from isolated leaves.

Two PLS-R models were built without and with oblique projection,

with a cross-validation error below 0.2% DM for both of them.

e 3.5 4 2.5 3 1.5 2 1 c b a L1 L2 L3 L1 L2 L3 L1 L2 L3 L1 L2 L3 d L1 L2 L3 e L1 L2 L3 f 0 0.5 1 1.5 2 2.5 3 3.5 4 1 2 3 4 5 6 7 8 9 10

Without projection With projection

0 0.5 1 1.5 2 2.5 3 3.5 4 1 2 3 4 5 6 7 8 9 10

Without projection With projection LNC prediction results (%DM) on hyperspectral images at the

leaf level.

a: ASD measurement positions and values.

b, c: Prediction on isolated leaves without (b) and with (c) oblique projection.

d, e, f: Prediction on the same leaves before cutting (natural image (d), image processed without (e) and with (f) oblique

projection

In order to confirm: the ten images combining both pot plants and isolated leaves have been

processed with both PLS-R models, and the LNC values have been averaged separately for

the two kinds of leaves

Both models give nearly the same results on isolated leaves, but very different ones on plant

images: PLS-R with no projection seems to generate an over prediction of LNC.

A new method has been proposed to overcome the impact of multiple scattering in the processing of hyperspectral images

of crop plants by PLS-R :

• It is based on the physical interpretation and formalization of scattering phenomenon

• Very promising results have been obtained on real durum wheat plants,

• The considerable impact of plant environment on the prediction of standard PLS-R has been confirmed as well.

Transmittance Multiple scattering

A systematic overestimation is obtained with the PLS-R model without projection for standing leaves

The two models were applied to the simulated spectra

issued from Caribu

The standard error is substantially

reduced by this projection (0.272% DM

instead of 0.422% DM).

Reference

AL MAKDESSI N., JEAN, P.-A., ECARNOT, M., GORRETTA, N., RABATEL, G. & ROUMET, P. 2017. How plant structure impacts the biochemical leaf traits assessment from in-field

hyperspectral images: A simulation study based on light propagation modeling in 3D virtual wheat scenes. Field Crops Research, 205, 95-105.

Near contributions and far contributions in Caribu simulation scheme.

Method development by simulation

In order to simulate spectra impacted by multiple scattering, a wheat canopy model has been built using simulation models included in the open source software platform Open-Alea:

Wheat canopy scene modeled using Adel-Wheat model

Reflectance +

Transmittance (Same for all leaves)

Adel + Caribu

Adel-Wheat Caribu

simulates the apparent reflectance of every visible

leaves in the canopy

Focal pot surrounded by 6 pots, in order to reproduce as much as possible the conditions

of multiple reflections.

Three individual leaves removed from the focal

pot and placed on black paper to be imaged without multiple

reflections.

Nitrogen measurement at 4 different points along each leaf, using

an ASD field spectrometer

reflections.

Method assessment in real conditions:

10 pots of durum wheat were imaged using a hyperspectral camera. For each pot:

Two PLS-R models were calibrated on the set

of basic spectra: Without and with oblique

projection

1.5 2 2.5 3 3.5 4 1.5 2 2.5 3 3.5 4 Actual E st im at ed

Test dataset (120 pts; 5 VL; R²:0.876; Err: 0.14)

1.5 2 2.5 3 3.5 4 1.5 2 2.5 3 3.5 4 Actual E st im at ed

Test dataset (120 pts; 6 VL; R²:0.75; Err: 0.199)

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Measured values Predicted v alu es R²=0.923 ; Bias=-0.0213 ; SEP_C=0.271 1 1.5 2 2.5 3 3.5 4 1 1.5 2 2.5 3 3.5 4 Measured values P re d ic te d v a lu es R2 = 0.904 ; Bias = 0.0445 ; SEP_C = 0.325

Some cases of leaf illumination in its natural environment. E: lighting source radiance. R: leaf reflectance. L: observed radiance. β1, β2, β3 are scalar factors related to leaf inclination.

Test data set (67984 pts; 5 Latent Variables) Test data set (67984 pts; 6 Latent Variables)

R² = 0,915; Bias = 0,0445; Sep = 0,422 R² = 0,936; Bias = 0,0213; Sep = 0,272

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