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Derivative-based band clustering and multi-agent PSO optimization for optimal band selection of hyper-spectral images

  • Kishore Raju KalidindiEmail author
  • Pardha Saradhi Varma Gottumukkala
  • Rajyalakshmi Davuluri
Article
  • 19 Downloads

Abstract

Images (HSIs) are popular in diversified applications, such as geosciences, biomedical imaging, molecular biology, agriculture, astronomy, food quality and safety assessment, surveillance and physics-related research. The rich spatial and spectral information of HSI is the key factors for robust representation of class-specific objects, in remote sensing applications. But, these images often suffer from Hughes effect. This effect causes the recording of information about a single scene in multiple spectral bands. This demands a dimensionality reduction step, which can either be a feature reduction/extraction or a feature selection. The feature selection process is commonly called band selection (BS) in the HS data set. The current study proposes an unsupervised BS technique, which is accomplished in three steps, including preprocessing of spectral bands, adjacent band clustering, and multi-agent optimization. Spatio-spectral (using a simple Gaussian filter) information is extracted to evaluate the performance using SVM classifier with different state-of-the-art band selection approaches. The performance of the proposed approach is evaluated for metrics including overall accuracy (OA), average accuracy (AA) and Kappa (\(\kappa\)). The experimental results are promising as these surpass that of other approaches.

Keywords

Dimensionality reduction Hyper-spectral image Optimization Sparse dictionary Unsupervised band selection 

Notes

Acknowledgements

We thank all the researchers whose work helped us to conclude the article.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Kishore Raju Kalidindi
    • 1
    Email author
  • Pardha Saradhi Varma Gottumukkala
    • 2
  • Rajyalakshmi Davuluri
    • 3
  1. 1.Department of Computer Science and EngineeringJawaharlal Nehru Technological University (JNTU)KakinadaIndia
  2. 2.Department of Computer Science and EngineeringK L Deemed to be UniversityGuntur DistrictIndia
  3. 3.Department of Computer Science and EngineeringUniversity College of Engineering JNTUK Narasaraopet (UCEN)NarasaraopetaIndia

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