In this paper, we present an image compression algorithm that is capable of significantly reducing the vast amount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet transform to remove the spatial redundancy. The transformed images are then encoded by Hilbert-curve scanning and run-length-encoding, followed by Huffman coding. We also present the performance of the proposed algorithm with the LANDSAT multispectral scanner data. The loss of information is evaluated by PSNR (peak signal to noise ratio) and classification capability.

Date Published: 21 December 1994
PDF: 11 pages
Proc. SPIE 2318, Recent Advances in Remote Sensing and Hyperspectral Remote Sensing, (21 December 1994); doi: 10.1117/12.197239

Published in SPIE Proceedings Vol. 2318:
Recent Advances in Remote Sensing and Hyperspectral Remote Sensing
Pat S. Chavez Jr.; Carlo M. Marino; Robert A. Schowengerdt, Editor(s)