Wavelet filter selection based on spectral features in multispectral image compression

Arto Kaarna; Jussi P. S. Parkkinen

doi:10.1117/12.413913

19 January 2001 Wavelet filter selection based on spectral features in multispectral image compression

Arto Kaarna, Jussi P. S. Parkkinen

Proceedings Volume 4170, Image and Signal Processing for Remote Sensing VI; (2001) https://doi.org/10.1117/12.413913
Event: Europto Remote Sensing, 2000, Barcelona, Spain

Abstract

The problem of selecting an appropriate wavelet filter is always present in signal compression based on the wavelet transform. In this report, we give a method to select a wavelet filter for multispectral image compression. The wavelet filter selection is based on the Learning Vector Quantization (LVQ). In the training phase for the test images, the best wavelet filter has been found by a careful compression-decompression evaluation. Certain spectral features are used in characterizing the pixel spectra. The LVQ is used to form the best wavelet filter class for different types of spectral images. When a new image is to be compressed, a set of spectra from that image is selected, the spectra are classified by the trained LVQ and the filter associated to the largest class is selected for the compression of the whole multispectral image. The results show, that our method finds the most suitable wavelet filter for compression of multispectral images.

Citation Download Citation

Arto Kaarna and Jussi P. S. Parkkinen "Wavelet filter selection based on spectral features in multispectral image compression", Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001); https://doi.org/10.1117/12.413913

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available