Modified Graph-Based Algorithm for Efficient Hyperspectral Feature Extraction

  • Asma FejjariEmail author
  • Karim Saheb Ettabaa
  • Ouajdi Korbaa
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 935)


Since the Laplacian Eigenmaps (LE) algorithm suffers from a spectral uncertainty problem for the adjacency weighted matrix construction, it may not be adequate for the hyperspectral dimension reduction (DR), classification or detection process. Moreover, only local neighboring data point’s properties are conserved in the LE method. To resolve these limitations, an improved feature extraction technique called modified Laplacian Eigenmaps (MLE) for hyperspectral images is suggested in this paper. The proposed approach determines the similarity between pixel and endmember for the purpose of building a more precise weighted matrix. Then, based on the obtained weighted matrix, the DR data are derived as the Laplacian eigenvectors of the Laplacian matrix, constructed from the weighted matrix. Furthermore, the novel proposed approach focuses on maximizing the distance between no nearby neighboring points, which raises the separability among ground objects. Compared to the original LE method, experiment results, for hyperspectral images classification and detection tasks, have proved an enhanced accuracy.


Laplacian eigenmaps Hyperspectral dimension reduction Endmember extraction Hyperspectral images 



This work was supported and financed by the Ministry of Higher Education and Scientific Research of Tunisia.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Asma Fejjari
    • 1
    Email author
  • Karim Saheb Ettabaa
    • 2
  • Ouajdi Korbaa
    • 1
  1. 1.MARS (Modeling of Automated Reasoning Systems) Research Laboratory. ISITComUniversity of SousseSousseTunisia
  2. 2.IMT Atlantique, Iti DepartmentTelecom BretagneBrestFrance

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