4D Cloud Scattering Tomography

Ronen, Roi; Schechner, Yoav Y.; Eytan, Eshkol

doi:10.1109/iccv48922.2021.00547

Cited by 12 publications

(12 citation statements)

References 42 publications

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“…Levis et al [31] and consequent works [68] on scattering tomography demonstrated methods experimentally on realworld data acquired by AirMSPI. We follow the same approach, which enables comparison to the prior art.…”

Section: Airmspi Real World Empirical Datamentioning

confidence: 99%

“…Because imaging is sequential, the scene shifts by global wind during the time it takes ER-2 to fly over the domain. So, we follow [68] to assess this 9. 3DeepCT is not used here, because 3DeepCT was neither designed nor trained for images taken by pushbroom or more general projection models: only on perspective inputs.…”

Section: Airmspi Real World Empirical Datamentioning

confidence: 99%

“…However, there is no ground truth information on this data (nor other relevant empirical data that sought). So, as in prior work, we check for consistency using cross-validation 10 [31], [39], [68]. For this, we excluded the +47 • view, trained VIP-CT and performed inference of β using only eight viewpoints.…”

Section: Airmspi Real World Empirical Datamentioning

confidence: 99%

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Variable Imaging Projection Cloud Scattering Tomography

Ronen¹,

Holodovsky²,

Schechner³

2024

IEEE Trans. Pattern Anal. Mach. Intell.

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Scattering-based computed tomography (CT) recovers a heterogeneous volumetric scattering medium using images taken from multiple directions. It is a nonlinear problem. Prior art mainly approached it by explicit physics-based optimization of image-fitting, being slow and difficult to scale. Scale is particularly important when the objects constitute large cloud fields, where volumetric recovery is important for climate studies. Besides speed, imaging and recovery need to be flexible, to efficiently handle variable viewing geometries and resolutions. These can be caused by perturbation in camera poses or fusion of data from different types of observational sensors. There is a need for fast variable imaging projection scattering tomography of clouds (VIP-CT). We develop a learning-based solution, using a deep-neural network (DNN) which trains on a large physics-based labeled volumetric dataset. The DNN parameters are oblivious to the domain scale, hence the DNN can work with arbitrarily large domains. VIP-CT offers much better quality than the state of the art. The inference speed and flexibility of VIP-CT make it effectively real-time in the context of spaceborne observations. The paper is the first to demonstrate CT of a real cloud using empirical data directly in a DNN. VIP-CT may offer a model for a learning-based solution to nonlinear CT problems in other scientific domains. Our code is available online.

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Section: Airmspi Real World Empirical Datamentioning

confidence: 99%

Section: Airmspi Real World Empirical Datamentioning

confidence: 99%

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Variable Imaging Projection Cloud Scattering Tomography

Ronen¹,

Holodovsky²,

Schechner³

2024

IEEE Trans. Pattern Anal. Mach. Intell.

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“…The temporal resolution of the retrieval depends on the frame-rate of data acquisition, while high synchronicity is achieved. If synchronicity is not practical, temporal accumulation of data of up to ≈ 30 [sec] may be applied [51].…”

Section: Stagementioning

confidence: 99%

“…The time-scale may reach ≈ 30 [sec]. As demonstrated by [51], this is an acceptable time scale regarding evolution of cloud droplet size.…”

Section: Cloudbow-scanmentioning

confidence: 99%

Settings for Spaceborne 3-D Scattering Tomography of Liquid-Phase Clouds by the CloudCT Mission

Tzabari

Holodovsky

Shubi

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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We introduce a comprehensive method for spaceborne 3D volumetric scattering-tomography of cloud microphysics, developed for the CloudCT mission. The retrieved micro-physical properties are the liquid-water-content and effective droplet radius within a cloud. We include a model for a perspective polarization imager, and an assumption of 3D variation of the re. Elements of our work include computed tomography initialization by a parametric horizontally-uniform micro-physical model. This results in smaller errors than the prior art. The mean absolute errors of the retrieved liquid-watercontent and effective-radius are reduced from 62% and 28% to 40% and 9%, respectively. The parameters of this initialization are determined by a grid search of a cost function. Furthermore, we add viewpoints in the cloudbow region, to better sample the polarized scattering phase function. The suggested advances are evaluated by retrieval of a set of clouds generated by large-eddy simulations.

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Tomography of Turbulence Strength Based on Scintillation Imaging

Shaul

Schechner

2022

Lecture Notes in Computer Science

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4D Cloud Scattering Tomography

Cited by 12 publications

References 42 publications

Variable Imaging Projection Cloud Scattering Tomography

Variable Imaging Projection Cloud Scattering Tomography

Settings for Spaceborne 3-D Scattering Tomography of Liquid-Phase Clouds by the CloudCT Mission

Tomography of Turbulence Strength Based on Scintillation Imaging

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