The key technology in hyperspectral remote sensing image compressiom is to get rid of the spatial correlation and spectral correlation. According to the characteristics of the structure of data of hyperspectral remote sensing image

how to effectively remove the spatial correlation and spectral correlation in hyperspectral image compression is a crucial problem. For hyperspectral remote sensing image

in the image coding

wavelet transform is a very effective method to remove redundancy. 3D wavelet transform can effectively remove the spatial correlation and spectral correlation of hyperspectral image. Therefore

this paper proposes a coding algorithm based on band ordering and 3D hybrid tree. First

the hyperspectral remote sensing image is divided into groups based on natural spectral order

each group contains 16 continuous spectral bands. Second

in each band group

the sum of the spectral correlation of all the adjacent bands is calculated. If the correlation value is less than a given threshold

a complete graph is structured on the basis of the group of bands. In the complete graph

each apex represents a band

so there are 16 apexes in the graph

and each side of the complete graph represents the spectral correlation value of two bands in the group. Then

the max Hamilton loop of the complete graph is searched for

and the group of bangs is reorderd based on the max Hamilton order to improve the correlation value of the band group. On the basis of the above

the reordered group of bands is given a 3D wavelet transform

and 3D hybrid tree coding algorithm is used to encoded the 3D wavelet transform coefficients. As to those band groups whose sum correlation values are greater than the given threshold

their band order does not have to be reordered. 3D wavelet transform will be given to these band groups directly

and then wavelet transform coefficients will be encoded by the 3D hybrid tree coding algorithm. First

reordering the band groups with a relatively small correlation value can improve the efficiency of 3D wavelet transform in removing the spectral correlation and spatial correlation of hyperspectral image. By reordering

3D wavelet can more effectively remove the redundancy. Second

the use of 3D hybrid tree coding algorithm can produce more effective zero trees because the group of band with a weak correlation value is reordered to improve the correlation value. Based on the algorithm above

the coding efficiency improves to a certain extent. AVIRIS hyperspectral images are used in the simulation experiment to verify the validity of the algorithm. Given the adoption of a reordering mechanism on the band groups that have weak spectral correlation values

the hybrid tree structure of the 3D zero tree coding algorithm appears more effective and to a certain extent

improve the coding efficiency. From the experimental data we can see that

compared with the use of wavelet transform only

joined band ordering and the hybrid tree structure coding algorithm improves the peak signal-to-noise ratio of the decoded image to some extent.