Sub-grid physical optical flow for remote sensing of sandstorm

Abstract

Satellite observations are currently of major importance in geosciences. Remote sensing is a strong tool to study atmospheric and earth phenomena. In this work, we propose a new motion estimation approach with application to dust storm tracking from remote sensing images. Dynamic natural phenomena in the atmosphere are generally turbulent due to a high Reynolds number. Meteorological images are still poor in time and space resolution compared to the turbulence characteristics of the flow. To tackle this problem, we define a new formulation of the flow equation based on a filtered scalar transport equation. Using Large Eddy Simulation theory, we propose a sub-grid model which incorporates small scale effects as missing (ie non-observed) information of remote sensing images. For day light changes, a uniform brightness variation term is incorporated to the model. We validated our approach on synthetic Direct Numerical Simulation (DNS) of scalar propagation. Promising results are obtained on real MTSAT-1R visible images of a dust storm event over Australia.

Satellite Angular Velocity Estimation Based on Star Images and Optical Flow Techniques

“…Besides being adopted for vision-based guidance and control of Unmanned Aircraft Systems, optical flow techniques have found usage in space applications within the fields of remote sensing and space exploration. Regarding spaceborne remote sensing, optical flow measurements have been used for example to estimate glacier motion from multi-temporal sequences of electro-optical (EO) images [ 7 ], to detect sandstorms [ 8 ], to estimate atmospheric motion from geostationary meteorological satellites [ 9 ]. Within space exploration, optical flow approaches have been widely proposed for planetary landing (see for example [ 10 , 11 ]).…”

Motion Estimation for Dynamic Texture Videos Based on Locally and Globally Varying Models

“…In recent years, various physics based methods of estimating optical flow from a fluid-like/DT image sequence have been widely reported [6]- [11], [15], [27], [29], [32], [41], [48], [52], where slowly moving objects with only slight local illumination and texture changes are often used: i.e., satellitecaptured climate related patterns. From the fluid dynamic (FD) viewpoint, a continuity equation (CE) [8], [9], [12] has been replaced by a previous IBC.…”

“…State-of-the-art Navier-Stokes equation (NS) based studies [10], [11], [22], [32], [35], [36], [41], [48], [52], incorporate the full or simplified FD models into a minimization framework of optical flow. Since several global model parameters must be empirically tuned in advance, it is difficult for these works to offer parameter turning in the presence of locally abrupt local changes, i.e., discontinuous motion in a long-term surveillance video.…”

“…However, because of the necessity and importance, the conventional approach is to use a cross-correlation based method [15] for the rough detection of the motion and object deformation such as atmospheric patterns. For more refined motion estimation, a large number of optical flow models [5], [7], [35]- [50], [52] have been applied to a variety of images with different properties ranging from rigidity, elasticity, to fluidity, and/or from sparsely to densely moving objects. Related to these properties, image sequences with apparent motion, shape, and texture changes are often called fluid-like images/dynamic texture (DT) (images) [1]- [3].…”

“…Most previous optical flow models pose smoothing based constraints on image intensity and/or velocity, and then solve for the objective function to be minimized with global model parameter estimation/tuning. In particular, in order to capture an underlying physical property, i.e., natural phenomena, of moving scenes/objects, physics based models such as fluid dynamics (FD) [7], [8], [38], [39], [41], [48], [52] have been applied: examples include continuity equations, a simplified Navier-Stokes (NS) equation [41], and divergence-curl velocity (DC) [17]. As used in FD, the fluidity of images can be roughly quantized by the ratio of inertial force to viscosity force, which is defined in terms of low Reynolds number (Re) and/or incompressibility.…”