Remote sensing image compression based on a morphological wavelet coding

Wenbo Wu; Fuxiang Hu; Zhigao Yang; Qianqing Qin

doi:10.1117/12.655075

3 November 2005 Remote sensing image compression based on a morphological wavelet coding

Wenbo Wu, Fuxiang Hu, Zhigao Yang, Qianqing Qin

Proceedings Volume 6044, MIPPR 2005: Image Analysis Techniques; 604410 (2005) https://doi.org/10.1117/12.655075
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

This paper addresses the problem of image compression in remote sensing applications. Compared with other still images, remote-sensing images are characterized with complex textures and weak local correlation. By using wavelet transform, the coefficients have showed a spatial clustering trend in wavelet domain. Most of current algorithms of image compression have not taken this clustering into account. In order to further improve coding efficiency, an efficient remote sensing image coding algorithm based on morphological wavelet is proposed. First, the fast multi-scale wavelet transform is applied to image; second, a morphological operator is designed to capture the clusters and fully exploit the redundancy between the coefficients. Compression is then achieved by using this non-linear method. For multi-bands remote-sensing images, a Prior Important Band (PIB) method is used to decorrelate the correlations in the spectral dimension, the above coding algorithm is then applied to the bands. In the experiment, the author selects one AVARIS hyper-spectral image and two satellite images to test the performance of the algorithm. Experimental results illustrate that it provides higher performance than JPEG2000 in low-bits compression and it is suitable to multi-band images too.

Citation Download Citation

Wenbo Wu, Fuxiang Hu, Zhigao Yang, and Qianqing Qin "Remote sensing image compression based on a morphological wavelet coding", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 604410 (3 November 2005); https://doi.org/10.1117/12.655075

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