23-029 Atmospheric Correction of HR Images Using

Il y a 2 mois


Paris, France CNES - Centre National d'Etudes Spatiales Temps plein

Doctorat, 36 mois
- Temps plein
- Moins de 2 ans d’expérience
- Master, DESS, DEA, Bac+5
- Physical principles and image quality

**Mission**:
Atmospheric correction aims to retrieve the surface reflectance (SR) at the bottom of the atmosphere (BOA) from remote sensing data by estimating atmospheric effects in order to correct them. This is a critical step in remote sensing. Most biophysical/biochemical variable inversion algorithms are based on SR. The correction of atmospheric effects is also a widespread need for orthophotography (obtaining a homogeneous mosaic), change detection, and land cover mapping.
- Multispectral satellites like MODIS and Sentinel-2 are able to provide medium resolution (>10m) SR images (level 2A product) to users. However, the atmospheric correction of high resolution (HR) satellite (Pléiades Neo, Pléiades HR, CO3D, CSO,...) and aerial (UltraCam, DMC,...) images remains an open issue.

The main problems are that:

- These HR sensors do not have specific spectral channels dedicated to the estimation of atmospheric parameters;
- HR images are more impacted by directional effects due to the wider field of view (FOV);
- Aerial acquisition missions often use different acquisition parameters, which make it difficult to calibrate the sensors.

Objectives: We consider that the three impacts mentioned above produce two types of effects in a photogrammetry image: uniform effects and non-uniform effects. Uniform effects are related to atmospheric reflection, atmospheric optical thickness, total irradiance at BOA, and sensor calibration coefficients. Non-uniform effects are related to atmospheric transmittance, bidirectional reflectance, BOA irradiance of complex surfaces (tilted surfaces and shaded surfaces). To correct these two types of effect, we propose the following tasks:
Task 1 will consist in defining the features and selection criteria to locate reference targets. The objective here is to study the reliability of such reference targets in relation to the impact of spatial homogeneity in the satellite image pixel and the impact of the bidirectional reflectance. This information will be provided by HR images. The reference SR on these targets will come from L2A products from the Sentinel-2 (10m and 20m). In practice, it is difficult to find targets with perfect Lambertian property. The error budget will be studied using image simulations. We propose to update the multi-rule change detection approach [3] with these criteria to determine the reference targets. These targets are used to correct the uniform effects in the images using the linear relationship between the pixel value in digital count (DN) of the image and the SR.

Task 2 will study the differences in radiometric characteristics between two sensors [4]. It will be necessary to take into account the misalignment of the spectral response and the difference of bandwidth between these two types of sensor.

Task 3 aims to the correction of directional effects. The HR images are acquired with a high overlap rate (multi-view measurements). We aim to normalize the directional reflectance at each pixel using the available viewing directions. Homogenization techniques will be applied to remove residues. Atmospheric parameters can be adjusted for better taking into account the angles of view.

Task 4 will consist in studying the separation of the components of sky diffuse irradiance and sun direct irradiance once the total BOA irradiance is estimated [5]. Estimating the amount of diffuse sky irradiance is indeed essential to correctly estimate the reflectance of complex scenes with both tilted and shaded surfaces. The proposed approach will be applicable to very high spatial resolution (VHSR) satellite imagery (Pléiades Neo, Pléiades HR, CO3D, CSO,...) and aerial imagery (UltraCam, DMC,...).

==

**Profil**:
Master 2
student in remote sensing, physics. A good background in remote sensing,
radiative transfer; radiometric correction is required, and a knowledge on
change detection will be appreciated. Mastery of Python. Fluency in English.
(Optional) Experienced with aerial/satellite sensor technology, image
simulation, DART simulation.

**Infos pratiques**:
LASTIG

**Mot du recruteur**:


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