The estimation of vegetation traits, which is essential to characterize the health of trees from remote sensing data, presents several challenges in urban environments, due to the topography of 3D buildings and associated shading, the spectral diversity of materials, or the variety of urban morphology. Moreover, the difficulty to estimate the vegetation traits increases with the decrease of spatial resolution, mixed pixels including information on trees and their environment. The objective of this study is to estimate the influence of tree-endogenous (chlorophyll, LAI...) and tree-exogenous (urban form, tree distance to buildings, street orientation, solar angles, material types...) factors on the reflectance of Sentinel-2 pixels (10/20 m resolution). For this, a sensitivity analysis was carried out with the DART 3D radiative transfer model. First, a design of experiments was built using 15 variables describing the trees and their environment. Four urban 3D scenes that were elaborated based on the Local Climate Zone (LCZ) typology. For each of these urban 3D scenes, 3000 simulations were generated. Then, Sobol indices were computed to estimate the influence of each factor on the Sentinel-2 reflectance, more specifically on the ten spectral bands and eight vegetation indices correlated to vegetation traits. These experiments were conducted on isolated and aligned trees. In addition, the influence of the geo-registration uncertainty of the Sentinel-2 products was assessed in comparing the results obtained using a single tree-centered pixel with those using pixels offset from the tree. Results showed that Sentinel-2 data at 10 m resolution, NDVI et ARVI indices are the most relevant for the estimation of vegetation traits both for isolated and aligned trees, especially in LCZ five and eight, and in using a single tree-centered pixel approach.
With the development of optical links for space-ground communications comes the need to mitigate the effects of the atmospheric turbulence to guarantee a lossless connection. By having a network of addressable ground stations we want to guarantee to always target a point where the link is available. For this to work, we need to be able to predict the forthcoming link availability for each station while keeping costs low. We have developed a method that allows, in the geostationary case, to obtain the power margin on the link without exhaustive knowledge of the turbulence state. In this work, we show that the sole knowledge of 4 integrated parameters of the turbulence profile (Cn²) and associated wind profile, which can be measured with low-complexity instruments, provides us with enough information to entirely describe the statistics of the received optical power after an adaptive optics correction. We further develop the method to take into account digital mitigation techniques (interleaving and numerical error correction) and obtain the link power margin with a maximum error compatible with current assumptions made in commonly used link budgets. Additional presentation content can be accessed on the supplemental content page.
Infrared focal plane arrays (IRFPA) are widely used to perform high quality measurements such as spectrum acquisition at high rate, ballistic missile defense, gas detection, and hyperspectral imaging. For these applications, the fixed pattern noise represents one of the major limiting factors of the array performance. This sensor imperfection refers to the nonuniformity between pixels, and is partially caused by disparities of the cut-off wavenumbers. In this work, we focus particularly on mercury cadmium telluride (HgCdTe), which is the most important material of IR cooled detector applications. Among the many advantages of this ternary alloy is the tunability of the bandgap energy with Cadmium composition, as well as the high quantum efficiency. In order to predict and understand spectral inhomogeneities of HgCdTe-based IRFPA, we propose a modeling approach based on the description of optical phenomena inside the pixels. The model considers the p-n junctions as a unique absorbent bulk layer, and derives the sensitivity of the global structure to both Cadmium composition and HgCdTe layer thickness. For this purpose, HgCdTe optical and material properties were necessary to be known at low temperature (80K), in our operating conditions. We therefore achieved the calculation of the real part of the refractive index using subtracti
Infrared Focal Plane Arrays (FPA) are increasingly used to measure multi- or hyperspectral images. Therefore, it is crucial to control and modelize their spectral response. The purpose of this paper is to propose a modeling approach, adjustable by experimental data, and applicable to the main cooled detector technologies. A physical model is presented, taking into account various optogeometrical properties of the detector, such as disparities of the pixels cut-off wavelengths. It describes the optical absorption phenomenon inside the pixel, by considering it as a stack of optical bulk layers. Then, an analytical model is proposed, based on the interference phenomenon occurring into the structure. This model considers only the three major waves interfering. It represents a good approximation of the physical model and a complementary understanding of the optical process inside the structure. This approach is applied to classical cooled FPAs as well as to specific instruments such as Microspoc (MICRO SPectrometer On Chip), a concept of miniaturized infrared Fourier transform spectrometer, integrated on a classical Mercury-Cadmium-Telluride FPA, and cooled by a cryostat.
We present a compact real-time multispectral camera operating in the mid-infrared wavelength range. Multispectral images of a scene with two differently spectrally signed objects and of a burning solid propellant will be shown. Ability of real-time acquisition will thus be demonstrated and spectra of objects will be retrieved thanks to inversion algorithm applied on multispectral images.
A concept of Fourier-transform infrared spectrometer integrated on a focal plane array (FTIR-FPA) has been developed
for very fast acquisition of spectral signatures. The basic idea is to use the upper surface of the focal plane array as the
first mirror of a two-wave interferometer, which creates interference fringes directly inside the active layer. Two
technologies have been developed. In a "monolithic" version of our FTIR-FPA concept, the cavity is made by grinding
the substrate to the shape of a wedge. In a "hybrid" version, the cavity is made by hybridizing a Silicon plate just above
the focal plane array.
In this paper, we first report the recent achievement of a mid-infrared supercontinuum fiber laser source in our
laboratory. Using fluoride fibers, we have generated a wavelength supercontinuum covering the whole 2-3.5μm range,
and delivering a power spectral density of 0.3 mW/nm on a large spectral range. Experimental results are presented. This
source can open opportunities for broadband remote sensing of multiple gas species in the atmosphere, especially above
3 μm, where numerous organic compounds have strong absorption signatures. Therefore, we consider a simple
Supercontinuum Laser Absorption Spectroscopy (SLAS) experiment, and we develop a numerical case study above
3 μm, involving a multi-component gas mixture. We first describe a method for modelling noisy spectroscopic signals.
Then we consider the inverse problem, and attempt to perform identification and quantitative estimation of the gas
mixture. After showing the inapplicability of a direct multi-linear regression, we focus on processing methods that use
complexity penalization principles, and show that they can address efficiently the identification/estimation problem.
Among various penalization criteria, those based on Minimum Description Length (MDL) approaches are shown to
perform particularly well. Finally, we apply these methods to preliminary experimental spectroscopic signals obtained
with supercontinuum sources in our laboratory.
Existing computer simulations of aircraft infrared signature do not account for the dispersion induced by uncertainty
on input data, such as aircraft aspect angles and meteorological conditions. As a result, they are of little
use to estimate the detection performance of IR optronic systems: in that case, the scenario encompasses a lot
of possible situations that can not be singly simulated. In this paper, we focus on low resolution infrared sensors
and we propose a methodological approach for predicting simulated infrared signature dispersion of poorly
known aircraft, and performing aircraft detection and classification on the resulting set of low resolution infrared
images. It is based on a Quasi-Monte Carlo survey of the code output dispersion, on a new detection test taking
advantage of level sets estimation, and on a maximum likelihood classification taking advantage of Bayesian
dense deformable template models estimation.
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