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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Extraction: Partition and Distribution Coefficients01:14

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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
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The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
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Mass Analyzers: Common Types01:19

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The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
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AGDF-Net: Attention-Gated and Direction-Field-Optimized Building Instance Extraction Network.

Weizhi Liu1, Haixin Liu1, Chao Liu1,2

  • 1College of Mining and Geomatics, Hebei University of Engineering, Handan 056038, China.

Sensors (Basel, Switzerland)
|July 29, 2023
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Summary

This study introduces AGDF-Net for accurate building extraction from remote sensing images. The novel network enhances feature integration and refines segmentation masks, significantly improving performance over existing methods.

Keywords:
attention gatebuilding extractiondirection fieldinstance segmentation

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Geospatial Analysis

Background:

  • Accurate building extraction from high-resolution remote sensing images is crucial for urban planning and population estimation.
  • Challenges include intraclass heterogeneity, interclass homogeneity, complex backgrounds, and maintaining regular building boundaries.

Purpose of the Study:

  • To propose an Attention-Gated and Direction-Field-Optimized Building Instance Extraction Network (AGDF-Net) for improved building instance segmentation.
  • To enhance information flow between feature levels and optimize segmentation masks for greater accuracy.

Main Methods:

  • Developed an Attention-Gated Feature Pyramid Network (AG-FPN) to integrate low-level and high-level features based on information importance.
  • Introduced a Direction Field Optimization Module (DFOM) for pixel-level direction prediction and iterative mask correction.

Main Results:

  • AGDF-Net outperformed six state-of-the-art instance segmentation and three semantic segmentation methods.
  • Achieved improvements of 1.1%–9.4% in the objective-level metric Average Precision (AP) and 3.55%–5.06% in the pixel-level metric Intersection over Union (IoU).

Conclusions:

  • The proposed AGDF-Net effectively addresses challenges in building extraction from complex remote sensing imagery.
  • The AG-FPN and DFOM modules significantly enhance segmentation accuracy and boundary regularity, demonstrating superior performance.