Hyperspectral Image Classification With Online Structured Dictionary Learning

Saeideh Ghanbari Azar, Saeed Meshgini, Tohid Yousefi Rezaii, Ali Farzamnia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


In this study, the spectral and spatial redundancies of hyperspectral images are used for designing a sparse representation-based classification approach. The spectral redundancy is used to define spectral blocks and they are used to adaptively recognize the distinctive bands. The most distinctive blocks are identified as active blocks in a block sparse representation approach. Then the sparse coefficients within each spatial group are imposed to share a common subspace. To achieve this hierarchical sparsity pattern a sparse coding algorithm is proposed. This sparse coding is done over a block-structured dictionary, which is learned from the image data using the online dictionary learning algorithm. The obtained sparse coefficients are then classified using a support vector machine classifier. This structured sparsity pattern alleviates the instability of the sparse coefficients. Experiments on two standard datasets namely, Indian Pines and Pavia University, verify the effectiveness of the proposed approach for the classification of hyperspectral images.

Original languageEnglish
Title of host publication2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728150758
ISBN (Print)9781728150765
Publication statusPublished - 23 Jan 2020
Externally publishedYes
Event9th International Conference on Computer and Knowledge Engineering - Mashhad, Iran, Islamic Republic of
Duration: 24 Oct 201925 Oct 2019
Conference number: 9

Publication series

NameInternational Conference on Computer and Knowledge Engineering, ICCKE
ISSN (Print)2375-1304
ISSN (Electronic)2643-279X


Conference9th International Conference on Computer and Knowledge Engineering
Abbreviated titleICCKE 2019
Country/TerritoryIran, Islamic Republic of

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