Research Of Hyper-spectral Imagery Compression Approach Based On Adaptive Band Regrouping

In recent years, with the development of both satellite remote sense techniques, the applications based on hyper-spectral image become more and more popular, such as earth resource exploration, environmental monitoring and military reconnaissance. However, the amount of hyper-spectral data increases with the resolution of images. There are many challenges for image processing and channels transmission on satellite. Firstly, the environmental condition is harsh and the collection of image is generally expensive, so the information loss should be minimized during the image processing. Secondly, the processing capability of on-board platform is limited, particularly the low-power consuming devices have not enough storage space, and so the collection of data must be compressed before being transferred to the earth. Thirdly, there is a big contradictory between the limited communication capacity of satellite channel and large amount of hyper-spectral data, so it needs a tradeoff between data bit rate and the quality of image. Finally, satellite channels have high uncertainty. Therefore, the remote sensing data coding should have the ability of fault tolerance.All above factors require the cooperation of coding efficiency and coding quality so as to satisfy different needs of applications. Based on the framework of hyper-spectral image compression system, this paper aims at improving the efficiency and speed of real-time image coding by developing some new algorithms of these two techniques: (1) Band pre-processing algorithms for hyper-spectral image compression. To remove the spectral correlation and improve spectral coding efficiency, this paper studies the applicable spectrum regroup scheme for predictive coding and integer transform. (2) Spatial and spectral algorithms for hyper-spectral image compression. Based on the still image coding algorithms and the characteristics of hyper-spectral image, this paper investigates special and spectral encoding module, which is applied in processing of satellite hyper-spectral data. Specific research work is as follow.First, the basic theory of hyper-spectral coding is introduced and described. The framework and the main function module of the system are given. Based on this framework, the basal theory of the main modules is expatiated, including traditional prediction, transform algorithm, quantization and entropy coding. Second, research for band pre-processing of hyper-spectral image compression. Summing up the state of the art in band pre-processing algorithm and analyzing spatial and spectral characteristics of the hyper-spectral data, we propose an improved C-Mean clustering algorithm. On this basis, this paper proposes a new algorithm based on adaptive band regrouping and designs fast algorithm. The experimental results show that the proposal can maintain the quality of image under low computational complexity. So it is an efficient algorithm; the performance of the revised C-Mean coding algorithm has been improved.Third, research for spectral coding algorithms of hyper-spectral image compression. Based on the conclusion of the current spectral coding algorithm, this paper presents a detailed linear regroup algorithm. Based on the algorithm, the optimal linear prediction program and reversible spectral dimension integral DCT coding are designed. The experimental results show that these algorithms can greatly improve the performance of the spectral de-correlation algorithm.Fourth, research for spatial coding algorithms of hyper-spectral image compression. Based on a simple analysis of DCT and DWT, this paper proposes a new special coding program based on two-dimensional integer partial transform coding and trellis coded quantization. The experimental results show that the algorithm has superior performance by combining trellis coded quantization and DCT technique.Finally, the complete hyper-spectral image compression system is designed. This system has a module of handling abnormal. Considering the performance evaluation of the compression system, this paper designs a simple integrating platform with some subjective and objective quality criteria benchmark. The experimental results show that the new compression system has a good performance based on the evaluation platform. The research work involves the techniques in compression domains of hyper-spectral image; therefore, it has important theoretic and practical significances.