| Remote sensing data is reflected by the same object on different bands of electromagnetic waves,and it contains a wealth of geographic information.Remote sensing data are widely used in resource exploration,climate survey,surveying and mapping,water conservancy and so on.However,the satellite is always shooting the ground material,the remote sensing data is very large,it is very difficult to transmit the remote sensing data.So it is necessary to compress the remote sensing data to speed up the transmission rate and reduce the waste of transmission resources.Local prediction,the combination of wavelet and fractal,and compression algorithm based on compression perception are studied in this paper.Remote sensing image is compressed effectively,focusing on the correlation of the spatial structure and inter-spectral structure.The main contents of this paper are:1、Research data in this paper is the remote sensing image of Yangyuan County of Zhangjiakou City.The characteristics of the spatial and spectral correlation of the image are studied in this paper,and a local prediction compression algorithm for remote sensing image is proposed.Firstly,the original image is divided into several subgraphs according to the types of the materials contained in the remote sensing image.Then,the different bands are predicted by dual-band and single-band for different sub-images.The experimental results show that high compression ratio is obtained by different compression methods for remote sensing images with different kinds of materials.2、The compression method based on lifting wavelet transform and fractal is studied in this paper.The large amount of data of remote sensing image,the large compression ratio of fractal coding and the characteristics of wavelet transform are analyzed.At first,the original remote sensing image is converted a low-frequency component and three high-frequency components using a lifting wavelet transform;Then,the low-frequency part is processed by fractal compression using the minimum mean square error search.Experimental results show that the compression time of lifting wavelet transform and improved fractal compression method is shortened by 8 times,and the quality of reconstructed image has also been improved,compared with the wavelet transform and fractal combined compression method.3、A new compression and reconstruction method combining wavelet transform with compression perception is proposed according to the theory of compression perception and wavelet transform.The original image is decomposed by a layer of wavelet decomposition.Secondly,high frequency components are converted to three sparse matrices using sparse representation.Then,three sparse matrices are reconstructed by subspace tracking algorithm.Finally,the reconstructed image is reconstructed by inverse wavelet transform of original low frequency part and the three reconstructed components.Simulation results show that the quality of reconstructed image by the subspace tracking algorithm is better,and the signal to noise ratio is higher,compared with orthogonal matching tracking method.In this paper,a variety of compression algorithms based on spatial and inter-spectral structural characteristics of remote sensing images,discrete wavelet transform,fractal coding and compression sensing is proposed.Experimental results show that the improved compression algorithm can obtain better compression results,which is of great significance in remote sensing data compression. |
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