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High-Resolution Remote Sensing Image Classification with RmRMR-Enhanced Bag of Visual Words.

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This study introduces a novel algorithm to reduce redundant visual words in remote sensing image classification. The method enhances classification precision by improving feature descriptive power.

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Area of Science:

  • Computer Science
  • Remote Sensing
  • Machine Learning

Background:

  • Remote sensing image classification relies on effective feature representation.
  • High information redundancy in visual words can degrade classification performance.
  • Existing methods may not adequately balance feature relevance and redundancy.

Purpose of the Study:

  • To propose a novel algorithm for filtering redundant visual words in remote sensing image classification.
  • To improve the discriminative power of visual word dictionaries.
  • To enhance the overall classification accuracy of high-resolution remote sensing images.

Main Methods:

  • A ReliefF improved mRMR (RmRMR) criterion-based bag of visual words (BoVW) algorithm is developed.
  • The ReliefF algorithm assigns weighting parameters to represent the contribution of each visual word.
  • The mRMR criterion, with a dictionary balance coefficient, calculates word relevance and redundancy.
  • A dictionary discriminant function is established to filter discriminative dictionary subsets.

Main Results:

  • The proposed RmRMR algorithm effectively reduces redundant information in the visual word dictionary.
  • The algorithm achieves a better balance between the relevance and redundancy of visual words.
  • Feature descriptive power of dictionary subsets is enhanced.
  • A marked increase in classification precision for high-resolution remote sensing images is observed.

Conclusions:

  • The RmRMR algorithm offers an effective approach to optimize visual word dictionaries for remote sensing.
  • This method improves feature selection by balancing relevance and redundancy.
  • The enhanced dictionary subsets lead to significantly improved classification accuracy.