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Application of Multiple-Optimization Filtering Algorithm in Remote Sensing Image Denoising.

Xuelin Zhang1, Yuan Li1, Xiang Feng1

  • 1University Research Center of Agricultural Remote Sensing and Precision Agriculture Engineering in Yunnan Provincial, School of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China.

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|September 28, 2023
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Summary
This summary is machine-generated.

A new multiple-optimization bilateral filtering (MOBF) algorithm effectively denoises remote sensing images without parameter input. This advanced method significantly improves image quality over traditional techniques, enhancing remote sensing applications.

Keywords:
Gaussian noisebilateral filteringdifferential evolution algorithmedge detection operatorremote sensing imagery

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Area of Science:

  • Remote Sensing
  • Image Processing
  • Computer Vision

Background:

  • Denoising remote sensing images is critical for accurate analysis and applications.
  • Gaussian noise is a prevalent issue in remote sensing imagery due to sensor, transmission, and environmental factors.

Purpose of the Study:

  • To develop an automated and effective algorithm for denoising remote sensing images.
  • To introduce a novel multiple-optimization bilateral filtering (MOBF) algorithm that requires no parameter input.

Main Methods:

  • Proposed a multiple-optimization bilateral filtering (MOBF) algorithm integrating edge detection and differential evolution (DE).
  • Optimized spatial domain filtering and Gaussian kernels using edge response standard deviation and width.
  • Employed DE for iterative refinement of solution vectors and optimal color space selection for pixel range domain kernel optimization.

Main Results:

  • The MOBF algorithm successfully denoises remote sensing images without requiring manual parameter tuning.
  • Experimental evaluation using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) demonstrated superior performance.
  • MOBF outperformed traditional denoising algorithms across all tested evaluation metrics.

Conclusions:

  • The proposed MOBF algorithm is a feasible and effective solution for remote sensing image denoising.
  • The automated nature and superior performance of MOBF offer significant advantages for remote sensing data processing.
  • This research contributes a robust method for enhancing the quality of noisy remote sensing imagery.