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PDE-Constrained Scale Optimization Selection for Feature Detection in Remote Sensing Image Matching

Author

Listed:
  • Yunchao Peng

    (PipeChina Network Corporation Eastern Oil Storage and Transportation Co., Ltd., Xuzhou 221008, China)

  • Bin Zhou

    (School of Sciences, Southwest Petroleum University, Chengdu 610500, China
    Institute of Artificial Intelligence, Southwest Petroleum University, Chengdu 610500, China)

  • Feng Qi

    (PipeChina Network Corporation Eastern Oil Storage and Transportation Co., Ltd., Xuzhou 221008, China)

Abstract

Feature detection and matching is the key technique for remote sensing image processing and related applications. In this paper, a PDE-constrained optimization model is proposed to determine the scale levels advantageous for feature detection. A variance estimation technique is introduced to treat the observation optical images polluted by additive zero-mean Gaussian noise and determine the parameter of a nonlinear scale space governed by the partial differential equation. Additive Operator Splitting is applied to efficiently solve the PDE constraint, and an iterative algorithm is proposed to approximate the optimal subset of the original scale level set. The selected levels are distributed more uniformly in the total variation sense and helpful for generating more accurate and robust feature points. The experimental results show that the proposed method can achieve about a 30% improvement in the number of correct matches with only a small increase in time cost.

Suggested Citation

  • Yunchao Peng & Bin Zhou & Feng Qi, 2024. "PDE-Constrained Scale Optimization Selection for Feature Detection in Remote Sensing Image Matching," Mathematics, MDPI, vol. 12(12), pages 1-16, June.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:12:p:1882-:d:1416245
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