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 shown a spatial clustering trend in wavelet domain.Most of current algorithms of image compression have not taken this clustering trend 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 the image

then 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 firstly used to decorrelate the correlations in the spectral dimension

and the above coding algorithm is then applied to the bands.In the experiment

the authors select one AVARIS hyper-spectral image and two satellite images to test the performance of the algorithm.Experimental results illustrate that its performance is superior to JPEG2000 in low-bits compression and it is suitable to multi-band images too.