3D wind field profiles from hyperspectral sounders: revisiting optic-flow from a meteorological perspective
In this work, we present an efficient optic flow algorithm for the extraction of vertically resolved 3D atmospheric motion vector (AMV) fields from incomplete hyperspectral image data measures by infrared sounders. The model at the heart of the energy to be minimized is consistent with atmospheric dynamics, incorporating ingredients of thermodynamics, hydrostatic equilibrium and statistical turbulence. Modern optimization techniques are deployed to design a low-complexity solver for the energy minimization problem, which is non-convex, non-differentiable, high-dimensional and subject to physical constraints. In particular, taking advantage of the alternate direction of multipliers methods (ADMM), we show how to split the original high-dimensional problem into a recursion involving a set of standard and tractable optic-flow sub-problems. By comparing with the ground truth provided by the operational numerical simulation of the European Centre for Medium-Range Weather Forecasts (ECMWF), we show that the performance of the proposed method is superior to state-of-the-art optical flow algorithms in the context of real infrared atmospheric sounding interferometer (IASI) observations.
Code (0)
등록된 구현이 없습니다.
Tasks
Optical Flow EstimationSimilar Papers 제목 키워드 기반
GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification
Hyperspectral image (HSI) classification is the most vibrant area of research in the hyperspectral community due to the rich spectral information contained in HSI can greatly aid in identifying objects of interest. Howev…
ClassificationHyperspectral Image Classificationimage-classificationImage ClassificationCatadioptric HyperSpectral Light Field Imaging
The complete plenoptic function records radiance of rays from every location, at every angle, for every wavelength and at every time. The signal is multi-dimensional and has long relied on multi-modal sensing such as hyb…
Machine Learning for Cloud Detection in IASI Measurements: A Data-Driven SVM Approach with Physical Constraints
Cloud detection is fundamental for the interpretation and operational exploitation of hyperspectral infrared sounders, yet the capability of infrared radiances alone to provide reliable cloud information remains insuffic…
Dimensionality ReductionWeather ForecastingFilter Selection for Hyperspectral Estimation
While recovery of hyperspectral signals from natural RGB images has been a recent subject of exploration, little to no consideration has been given to the camera response profiles used in the recovery process. In this p…
Dual Hyperspectral Mamba for Efficient Spectral Compressive Imaging
Deep unfolding methods have made impressive progress in restoring 3D hyperspectral images (HSIs) from 2D measurements through convolution neural networks or Transformers in spectral compressive imaging. However, they can…
Mamba