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Updated: Jan 25, 2026

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Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution

Xiaoqian Yang1, Zhenhong Jia2, Jie Yang3

  • 1College of Information Science and Engineering, Xinjiang University, Urumuqi 830046, China. m15099106737@163.com.

Sensors (Basel, Switzerland)
|May 1, 2019
PubMed
Summary

This study introduces a novel method for detecting changes in optical remote sensing images obscured by thin clouds. The approach effectively removes thin clouds and enhances change detection accuracy, improving image analysis.

Keywords:
FCM clusteringchange detectioncombination difference mapoptical remote sensing imagethin cloud removalunsupervised

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Area of Science:

  • Remote Sensing
  • Image Processing
  • Computer Vision

Background:

  • Thin clouds pose a significant challenge for accurate change detection in optical remote sensing images.
  • Existing methods often struggle to preserve image details while removing cloud interference.

Purpose of the Study:

  • To develop and evaluate a robust method for change detection in optical remote sensing images affected by thin cloud cover.
  • To address the limitations of current techniques in handling thin cloud interference.

Main Methods:

  • Thin cloud removal using wavelet coefficient substitution to suppress low-frequency cloud effects while preserving high-frequency image details.
  • Unsupervised change detection employing a combined difference graph and fuzzy c-means clustering (FCM) algorithm.
  • Image preprocessing including logarithmic transformation and Frost filtering for denoising.

Main Results:

  • The proposed wavelet-based method effectively removes thin clouds, maintaining crucial image details.
  • The combined difference graph and FCM clustering algorithm accurately identifies changes in processed images.
  • Experimental results demonstrate significant improvement in change detection performance under thin cloud conditions.

Conclusions:

  • The integrated approach of wavelet-based cloud removal and advanced clustering offers a reliable solution for change detection in thin cloud-affected remote sensing data.
  • This method enhances the utility of optical remote sensing for monitoring and analysis in challenging atmospheric conditions.