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A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs.

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Summary
This summary is machine-generated.

This study introduces a new module for processing Very High Resolution (VHR) satellite stereo imagery to create detailed Digital Elevation Models (DEMs). The method effectively handles large files and achieves high accuracy, with an RMSE of approximately 1 meter.

Keywords:
RANSACSIFTalgorithmdigital elevation modelsatellite stereo pair

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Area of Science:

  • Photogrammetry and Remote Sensing
  • Geomatics Engineering

Background:

  • Very High Resolution (VHR) satellite stereo-pair imagery offers significant potential for detailed topographic mapping.
  • Processing large VHR datasets for Digital Elevation Model (DEM) extraction presents computational challenges.

Purpose of the Study:

  • To present a novel module for VHR satellite stereo-pair imagery processing and DEM extraction.
  • To address the challenges of handling large VHR image file sizes and ensure accurate DEM generation.

Main Methods:

  • Utilized parallel processing of cascading image tiles to manage large VHR imagery.
  • Employed Scale-Invariant Feature Transform (SIFT) for feature matching and RANdom SAmple Consensus (RANSAC) for filtering feature pairs.
  • Extracted point cloud ground coordinates from homologous pairs and introduced average point spacing irregularity to assess DEM resolution.

Main Results:

  • Successfully processed a 0.5 m × 0.5 m Geoeye-1 stereo pair, generating a 1.5-m resolution DEM.
  • The resulting DEM exhibited superior detail compared to reference DEMs.
  • Achieved a Root Mean Square Error (RMSE) of approximately 1 meter against ground truth measurements.

Conclusions:

  • The developed module is effective for VHR satellite stereo-pair processing and DEM extraction.
  • The parallel processing approach efficiently handles large datasets.
  • The generated DEMs possess high accuracy and detail, suitable for various geomatic applications.