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Related Concept Videos

Boundary Layer Characteristics01:18

Boundary Layer Characteristics

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When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

59
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Influence of Earth's Curvature and Atmospheric Refraction on Leveling01:26

Influence of Earth's Curvature and Atmospheric Refraction on Leveling

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During leveling, the Earth's curvature and atmospheric refraction introduce deviations in the line of sight from a true horizontal reference. When the line of sight is leveled, it remains perpendicular to the plumb line only at a single point. Beyond this, it deviates due to the Earth’s curvature, represented by the correction C. For a sight distance D, the deviation can be derived using the relationship:This relationship shows that the deviation increases quadratically with distance.
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Variation of Atmospheric Pressure01:18

Variation of Atmospheric Pressure

2.0K
Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
Assuming the air temperature is constant at a given altitude and that the ideal gas law of thermodynamics describes the atmosphere to a good approximation, one can find the variation of atmospheric pressure with height.
Let p(y) be the atmospheric pressure at...
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Turbulent Flow01:24

Turbulent Flow

125
Turbulent flow is characterized by unpredictable fluctuations in velocity and pressure, which result in a chaotic fluid movement distinct from the orderly patterns of laminar flow. While laminar flow is governed by smooth, parallel layers with minimal mixing, turbulent flow exhibits highly irregular, three-dimensional patterns. This behavior arises due to instabilities in the fluid's velocity profile, and amplifies as the flow velocity increases. Minor disturbances, known as turbulent...
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Enhanced cloud removal via temporal U-Net and cloud cover evolution simulation.

Qingwei Tong1, Leiguang Wang2,3, Qinling Dai4

  • 1College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, Yunnan, China.

Scientific Reports
|February 6, 2025
PubMed
Summary

This study introduces a novel cloud removal method using cloud evolution simulation to improve remote sensing image quality. The approach enhances temporal information utilization for more accurate cloud predictions and better environmental monitoring.

Keywords:
Cloud cover evolution (CCE) moduleCloud removalRemote sensing imageResidual learningTemporal U-Net

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Area of Science:

  • Earth Observation
  • Environmental Monitoring
  • Image Processing

Background:

  • Cloud occlusion in remote sensing images degrades data quality for environmental monitoring.
  • Existing cloud removal methods struggle with artifacts, incomplete removal, and color distortion.
  • Limited sequential data hinders the use of temporal information for cloud removal.

Purpose of the Study:

  • To develop an advanced cloud removal method for remote sensing images.
  • To effectively utilize temporal information despite data scarcity.
  • To improve the accuracy and quality of cloud-free remote sensing data.

Main Methods:

  • Proposed a cloud removal method based on cloud evolution simulation.
  • Enabled construction of cloud evolution time-series without actual temporal data.
  • Embedded temporal information into a Temporal U-Net for enhanced cloud prediction.

Main Results:

  • Demonstrated significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).
  • Outperformed existing cloud removal techniques in extensive experiments.
  • Validated on RICE and T-CLOUD datasets.

Conclusions:

  • The proposed cloud evolution simulation method effectively addresses cloud occlusion in remote sensing.
  • Temporal information integration enhances cloud removal accuracy.
  • The method offers a robust solution for improving Earth observation data quality.