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Pairwise Elastic Net Representation-Based Classification for Hyperspectral Image Classification.

Hao Li1, Yuanshu Zhang2, Yong Ma2

  • 1School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China.

Entropy (Basel, Switzerland)
|August 27, 2021
PubMed
Summary

This study introduces Pairwise Elastic Net Representation-based Classification (PENRC) for hyperspectral image classification. PENRC improves accuracy by grouping correlated data and using local dictionaries, outperforming existing methods.

Keywords:
collaborative representationhyperspectral image (HSI) classificationneighbor informationpairwise elastic netsparse representation

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Machine Learning

Background:

  • Hyperspectral image (HSI) classification faces challenges with existing representation-based algorithms like sparse representation (SR) and collaborative representation (CR).
  • SR's l1-minimization may not capture full within-class information, while CR's l2-minimization can lead to mixed-class information due to using all dictionary atoms.
  • There is a need for improved methods that balance atom selection and utilize informative features for accurate HSI classification.

Purpose of the Study:

  • To propose a novel representation-based classification method, Pairwise Elastic Net Representation-based Classification (PENRC), for enhanced hyperspectral image classification.
  • To address the limitations of existing SR and CR methods by incorporating a similarity matrix for robust weight coefficient estimation.
  • To further enhance classification accuracy and reduce computational cost by introducing a joint approach (J-PENRC) that considers pixel neighborhood information.

Main Methods:

  • Developed the Pairwise Elastic Net Representation-based Classification (PENRC) method, combining l1 and l2 norms with a novel penalty term including a similarity matrix between dictionary atoms.
  • Implemented a local adaptive dictionary strategy using a subset of atoms to reduce computational complexity and improve robustness.
  • Introduced the Joint Pairwise Elastic Net Representation-based Classification (J-PENRC) method by integrating spatial-spectral neighborhood information into the PENRC framework.

Main Results:

  • Experimental results on hyperspectral datasets demonstrate that PENRC effectively groups highly correlated data, leading to more robust weight coefficients.
  • The local adaptive dictionary approach in PENRC significantly reduces computation cost while improving classification accuracy.
  • The J-PENRC method further boosts classification performance by leveraging contextual information from neighboring pixels, outperforming state-of-the-art algorithms.

Conclusions:

  • The proposed PENRC and J-PENRC methods offer significant improvements in hyperspectral image classification accuracy compared to existing techniques.
  • The integration of a similarity matrix and local adaptive dictionaries provides a more effective representation for HSI data.
  • Considering neighbor information in J-PENRC enhances robustness and classification performance, highlighting the importance of spatial-spectral feature integration.