Sparsity Regularization Based Spatial-Spectral Super-Resolution of Multispectral Imagery

  • Helal Uddin Mullah
  • Bhabesh DekaEmail author
  • Trishna Barman
  • A. V. V. Prasad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11941)


Multispectral (MS) remote sensing image is composed of several spectral bands of distinct wavelengths. Most earth observation satellites provide MS images consisting several low-resolution (LR) bands together with a single high-resolution (HR) image. A single image super-resolution (SISR) method tries to produce a HR MS output from the given LR MS input using digital image processing algorithms. In this work, we present a patch-wise sparse representation based MS image SR using a coupled overcomplete trained dictionary. The dictionary learning is carried out from patches extracted from the given HR panchromatic (PAN) image itself. Experiments are carried out using test MS images from QuickBird satellites and results are compared with other state-of-the-art MS image SR and pan-sharpening methods.


Super-resolution Sparse representation Dictionary learning Pan-sharpening Spectral information 



Authors would like to thank ISRO for providing funds under the RESPOND project (ISRO/RES/4/642/17-18) and Ministry of Electronics and Information Technology (MeiTY), GoI for providing financial support under the Visvesvaraya Ph.D. Scheme for Electronics & IT (Ph.D./MLA/ 04(41)/2015-16/01) which helped in smooth conduction of the above research work.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Helal Uddin Mullah
    • 1
  • Bhabesh Deka
    • 1
    Email author
  • Trishna Barman
    • 1
  • A. V. V. Prasad
    • 2
  1. 1.Department of Electronics and Communication EngineeringTezpur UniversityTezpurIndia
  2. 2.National Remote Sensing CentreHyderabadIndia

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