Dimension reduction and pre-emphasis for compression of hyperspectral images

C. Lee, E. Choi, J. Choe, T. Jeong

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

As the dimensionality of remotely sensed data increases, the need for efficient compression algorithms for hyperspectral images also increases. However, when hyperspectral images are compressed with conventional image compression algorithms, which have been developed to minimize mean squared errors, discriminant information necessary to distinguish among classes may be lost during compression process. In this paper, we propose to enhance such discriminant information prior to compression. In particular, we first find a new basis where class separability is better represented by applying a feature extraction method. However, due to high correlations between adjacent bands of hyperspectral data, we have singularity problems in applying feature extraction methods. In order to address the problem, we first reduce the dimension of data and then find a new basis by applying a feature extraction algorithm. Finally, dominant discriminant features are enhanced and the enhanced data are compressed using a conventional compression algorithm such as 3D SPIHT. Experiments show that the proposed compression method provides improved classification accuracies compared to the existing compression algorithms.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAurelio Campilho, Mohamed Kamel
PublisherSpringer Verlag
Pages446-453
Number of pages8
ISBN (Print)3540232400, 9783540232407
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3212
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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