Research On Compression Algorithm Of Hyperspectral Remote Sensing Image Based On Spectral Feature

Hyperspectral remote sensing based on the research of cross-discipline including the electromagnetic spectrum, geographic information systems, electronic technology, computer technology, aerospace technology and other subjects, emerges as a novel remote sensing technology and develops rapidly in recent years. Spectral resolution of the hyperspectral remote sensing, which is higher than 1% of wavelength, hits nanometer (nm) order of magnitude, and the number of spectral channels reaches up to tens or even hundreds. It organically combines the ground object spectra determined by the material composition and the space imaging inflecting the existed pattern of ground object and each pixel of space imaging can be assigned its own information of characteristic spectrum, which may improve greatly cognitive ability of the objective world. Hence, hyperspectral remote sensing has great value of application in various areas, such as survey of land resources, forestry remote sensing, environmental monitoring, agricultural applications, the space environment, and military target detection.However, with the spectral resolution of the hyperspectral ascended, the image that contains a wealth of information of ground object and spectra brings out mass data, which leads to engender hosts of difficulties in the transmission, storage and processing of image and restricts the pace of progress in the application of hyperspectral remote sensing. In other words, how to fully utilize the potential advantages of hyperspectral images and proposing effective sampling and compression methods of hyperspectral images have important theoretical significance and practical value. Therefore, this paper, which is based on the research of the spectral image compression technology of predecessors and combined with the characteristics of hyperspectral images, proposes some novel approaches of hyperspectral image compression, and these approaches perform well.Firstly, this paper makes a detailed analysis of the hyperspectral image characteristics as the basis for follow-up compression methods. More precisely, hyperspectral image has hundreds of spectral bands and a strong correlation between spectral in terms of the common image, beside, it can be found from the data that the correlation between spectral is much larger than the spatial correlation of image. In terms of the spatial correlation, the spectral image is apparently lower than the common image due to the large coverage area of ground object. Moreover, in the analysis of correlation, it can be seen that the spectral image itself has the local non-stationary characteristics, which is obvious different from the multispectral image. Consequently, these compression approaches that are applied to common image and multispectral image are not fully suitable for the hyperspectral image. In order to obtain the idea rate of compression, correlation between spectral and local non-stationary characteristics of hyperspectral image should be sufficiently used. The hyperspectral image also has the character of high-dimensional data, and conclusion that the high-dimensional spectral space is empty can be obtained by calculation. This provides a theoretical basis for the low-dimensional projection sampling compression method.Secondly, an embedded code has been applied on the compression of hyperspectral image and the design method with SPIHT (Set Partitioning in Hierarchical Trees) algorithm based on wavelet transform has been proposed. This method for compression makes efforts on performance and shortening time for coding and decoding. But the quality of image reconstruction using the method under low bit rate is not good. To solve this problem, the classic SPIHT algorithm is improved. The correlation hypothesis about neighbor nodes has been added to SPIHT algorithm and zero- tree's structure is also modified. In the meanwhile, it broaches the mind of LZC algorithm's symbol flag idea. Finally, the performance on hyperspectral image compression is improved.Thirdly, to improve the real-time performance of the current compression algorithms on hyperspectral image, a new lossless compression method based on prediction tree with error variances compensated for hyperspectral image is proposed in this paper. The method incorporates prediction tree and adaptive interband prediction techniques, and bidirectional interband prediction to current band is firstly applied to hyperspectral image compression. Then the error created by prediction tree is compensated by linear adaptive predictor which de-correlates spectral statistic redundancy. In consideration of the complexity for the coefficients' calculation, a correlation-driven adaptive estimator is designed to coefficients whose parameters are uniquely determined by the previously coded pixels. After de-correlating intraband and interband redundancy, an efficient wavelet coding method, SPIHT, is used to encode residual image. Finally, the proposed method in this paper achieves both low overhead and high compression ratio on data from the NASA JPL AVIRIS than current compression methods. At last, a novel theory of information acquisition-"compressive sampling" has been applied in this paper. The proposed approach offers a different perspective with regards to common wisdom in data acquisition of Shannon theorem. Common perception in compressed sensing indicates that one can recover certain signals and images perfectly from far fewer samples or measurements than traditional methods use. This paper presents an improvement on genetic algorithm instead of match pursuit algorithm in consideration of the enormous computational complexity on sparse decomposition. When applied to image data, our proposed approach divides the original scene into small blocks which can be processed by sparse decomposition, and a stopping criterion to decomposition process is determined by a peak signal to noise ratio (PSNR) threshold in adaptive fashion. Our experimental results indicate that good performance on image reconstruction with less computational complexity can be achieved.This paper centers on particular spectral feature of hypersectal image. Different compression algorithms proposed have been improved for need. The performance analyses and experiment evaluation are respectively given.