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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
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The polar coordinate system offers an alternative to the Cartesian coordinate system for specifying points in a plane, using a distance and an angle instead of x and y coordinates. This system is particularly advantageous in situations involving circular or rotational symmetry, such as in physics or engineering problems involving waves, oscillations, or orbital paths.Defining Polar CoordinatesIn polar coordinates, a point is represented as P(r, ��), where r is the radial distance...
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Related Experiment Video

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In Situ Soil Moisture Sensors in Undisturbed Soils
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Spatial Scale Effect of a Typical Polarized Remote Sensor on Detecting Ground Objects.

Ying Zhang1, Jingyi Sun1, Rudong Qiu1

  • 1School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a method to improve the accuracy of surface roughness inversion from polarized remote sensing data. By optimizing spatial scale using super-resolution reconstruction, it enhances remote sensor data for better analysis.

Keywords:
polarized remote sensorspatial heterogeneityspatial scale effectsuper resolution image reconstruction

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Area of Science:

  • Remote Sensing
  • Optics
  • Materials Science

Background:

  • Polarization images of ground objects vary with spatial scale due to target heterogeneity and sensor resolution limitations.
  • Spatial scale effects impact quantitative inversion accuracy in polarized remote sensing.

Purpose of the Study:

  • To investigate and mitigate the spatial scale effect in quantitative inversion for polarized remote sensors.
  • To improve the accuracy of surface roughness inversion for coatings using polarized remote sensing data.

Main Methods:

  • Surface roughness inversion using the polarized bidirectional reflectance distribution function (pBRDF) model.
  • Spatial scale effect correction via a linear-mixed pixel model.
  • Super-resolution image reconstruction using the projection onto convex sets (POCS) method.
  • Optimizing spatial scale using information entropy as an evaluation indicator.

Main Results:

  • Roughness inversion accuracy for coatings is highest at the optimal spatial scale.
  • The proposed method effectively reduces spatial effects in polarized remote sensing.
  • Super-resolution reconstruction enhances polarization imager resolution.

Conclusions:

  • The developed method offers a reliable approach to reduce spatial scale effects in polarized remote sensing.
  • Optimizing spatial scale significantly improves the accuracy of quantitative inversion, particularly for surface roughness.