We propose compression algorithms for hyperspectral images with enhanced discriminant features. As the dimension of remotely sensed images 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 features of the original data may be lost during the compression process. In this paper, we propose to apply preprocessing prior to compression in order to preserve such discriminant information. In particular, we enhance discriminant features before a compression algorithm is applied. Experiments show that the proposed method provides improved classification accuracies than the existing compression algorithms.
|Title of host publication||2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|ISBN (Electronic)||0780383508, 9780780383500|
|Publication status||Published - 2004|
|Event||2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data - Greenbelt, United States|
Duration: 2003 Oct 27 → 2003 Oct 28
|Name||2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data|
|Other||2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data|
|Period||03/10/27 → 03/10/28|
Bibliographical notePublisher Copyright:
© 2004 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications