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Methods of Obtaining Topography

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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
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Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
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Updated: Nov 26, 2025

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A Multiscale Topographical Analysis Based on Morphological Information: The HEVC Multiscale Decomposition.

Tarek Eseholi1, François-Xavier Coudoux1, Patrick Corlay1

  • 1Opto-Acousto-Electronics Department, Institute of Electronics, Microelectronics and Nanotechnology (IEMN), UMR-CNRS 8520, Polytechnic University of Hauts-de-France, Le Mont Houy, 59313 Valenciennes, France.

Materials (Basel, Switzerland)
|December 10, 2020
PubMed
Summary

This study introduces a novel HEVC Multiscale Decomposition (HEVC-MD) method for classifying material surface topographies. The HEVC-MD approach significantly improves classification accuracy, reaching up to 81% by analyzing specific filtered image data.

Keywords:
high-efficiency video coding (HEVC)mechanical engineeringroughness analysissupport vector machine (SVM)surface roughnesstexture feature descriptorstexture image classification

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Area of Science:

  • Materials Science
  • Computer Vision
  • Signal Processing

Background:

  • Accurate classification of material surface topographies is crucial for quality control and performance prediction.
  • Traditional methods using roughness parameters often lack sufficient discriminatory power.
  • Compressed-domain analysis offers potential for efficient feature extraction.

Purpose of the Study:

  • To evaluate the effectiveness of scale analysis and filtering in a compressed-domain classifier for material surface topographies.
  • To introduce and assess the performance of the HEVC Multiscale Decomposition (HEVC-MD) method.
  • To compare the proposed method against conventional roughness descriptors.

Main Methods:

  • Multiscale analysis using Gaussian filtering to decompose surface profiles into Low-pass (LP), Band-pass (BP), and High-pass (HP) filtered images.
  • Lossless compression of filtered images using the High-efficiency video coding (HEVC) standard.
  • Computation of Intra-Prediction Modes Histogram (IPHM) feature descriptors in the compressed domain and classification using Support Vector Machine (SVM).

Main Results:

  • A preliminary version achieved 52% accuracy.
  • Accuracy increased to 70% using multiscale analysis of High-pass (HP) filtered data.
  • Optimal classification accuracy of 81% was achieved by analyzing the highest-scale Low-pass (LP) filtered data.
  • The HEVC-MD method outperformed conventional roughness descriptors (increasing discrimination from 65% to 81%).

Conclusions:

  • The HEVC-MD method, leveraging compressed-domain analysis and multiscale decomposition, significantly enhances the accuracy of material surface topography classification.
  • Analysis of specific frequency ranges (highest-scale LP) is critical for optimal classification performance.
  • The proposed approach offers a superior alternative to conventional roughness parameter-based methods for surface discrimination.