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A new Best Scanline Determination (BSD) framework uses a Linear Regression Model for photogrammetric applications. This method significantly reduces computation time and complexity for processing pushbroom images in real-time.

Keywords:
best scanline search/determination (BSS/BSD)linear regression model (LRM)machine learningobject-to-image transformationphotogrammetrypushbroom imagery

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Area of Science:

  • Photogrammetry and Remote Sensing
  • Geomatics Engineering
  • Computer Vision

Background:

  • Linear array pushbroom images pose challenges for transforming object to image coordinates in photogrammetry.
  • Existing Best Scanline Determination (BSD) methods rely on iterative Collinearity Equation (CE) solutions, proving complex and time-consuming for real-time applications.
  • The need for efficient and accurate Exterior Orientation Parameter (EOP) determination for each scanline is critical.

Purpose of the Study:

  • To develop a novel Best Scanline Determination (BSD) framework with reduced computational complexity.
  • To eliminate the need for repetitive use of the Collinearitry Equation (CE) in scanline parameter determination.
  • To create a practical and robust solution for real-time photogrammetric applications using pushbroom imagery.

Main Methods:

  • A Linear Regression Model (LRM) based BSD framework was developed, utilizing Simulated Control Points (SCOPs) for training and Simulated Check Points (SCPs) for testing.
  • The method involves a two-phase approach: a training phase to calculate LRM parameters using SCOPs, and a test phase to evaluate accuracy and speed using SCPs.
  • The framework was evaluated on ten diverse pushbroom datasets, employing 5 million SCPs and a limited set of SCOPs.

Main Results:

  • The proposed LRM-based BSD method achieved a Root Mean Square Error (RMSE) on the order of 10^-9 pixels, demonstrating exceptionally high accuracy.
  • The method exhibited significantly improved robustness across various pushbroom images compared to existing BSS/BSD techniques.
  • Execution time was drastically reduced, requiring only 2-3 seconds, making it highly suitable for real-time applications.

Conclusions:

  • The novel LRM-based BSD framework offers a computationally efficient and highly accurate solution for determining Exterior Orientation Parameters (EOPs) in pushbroom imagery.
  • This approach overcomes the limitations of traditional iterative methods, providing a practical alternative for real-time photogrammetric tasks.
  • The method's speed, accuracy, and robustness make it a valuable advancement for processing linear array pushbroom data.