超光谱（Hyperspectral）成像具有高空间分辨率、高光谱分辨率和较多的光谱通道数。利用超光谱图像的高谱间分辨能够解决许多多光谱图像不能解决的问题。然而超光谱图像的高谱分辨力是以其较大的数据量及较高的数据维为代价的，这给超光谱图像的传输和存储都带来较大的困难，因此研究性能较高且简单可行的压缩算法对超光谱图像的应用具有重要意义。 本文针对遥感超光谱图像的特点提出了无损和近无损超光谱图像压缩方案。首先对超谱图像序列谱间采用DPCM编码处理，这主要是由于超谱图像的谱间具有很强的相关性 。紧接着分别采用整数（5.3）和（9.7）小波变换对残差图像进行无损和有损处理，整数小波变换的优点是具有简单的移位和加法操作等，而且比一般小波变换更适于消除遥感超光谱数据冗余，特别适合于需要实时、高速编码和无损压缩的场合，且利于今后的硬件实现。最后采用SPIHT算法对小波变换系数进行处理，SPIHT采用了空间方向树和两个集合更有效的表示小波系数结构，从而达到提高编码效率目的。 实验数据表明，本文基于SPIHT的超光谱图像压缩时，无损压缩比可达2.34，较算术编码提高了14.1%；在采用整数（9.7）小波变换有损压缩传输比特率为0.5bpp时，超谱序列图像平均峰值信噪比可达44.4376dB左右，较好地重建了图像，这说明本文的压缩方案对于超光谱图像的压缩效果较好。

Hyper-spectral imaging possesses the quality of high spatial resolution, high spectral resolution and more spectral channels. Many problems can be solved by using the high spectral resolution of hyper-spectral image while multi-spectral image can not. However, the high spectral resolution of hyper-spectral image is in the expense of greater data amount and bigger dimension, bringing difficulties in the transmission and storage of the image. Thus investigating compression algorithm that have higher performance and easy to implement is important to the application of hyper-spectral image. According to the characteristics of remote sensing hyper-spectral image compression, this thesis proposes lossless and near-lossless hyper-spectral image compression projects. Firstly, due to the strong correlation between the spectrums of hyper-spectral image, DPCM is used to process the sequence spectrum of the image. Then, integer (5.3) and (9.7) wavelet transform is used respectively to the processed image. For the easy operation of shifting and addition, and the advantage of eliminating redundancies of hyper-spectral remote sensing data more efficiently than common wavelet transform, integer wavelet transform is especially fit for data processing methods required to be real time, high speed coding and loss less compression. Finally, the wavelet transformed coefficients are processed by SPIHT algorithm. The coding efficiency is enhanced the fact that Spatial Orientation Tree and two sets are used in SPIHT to represent the structure of wavelet coefficients. Experiment results show that lossless compression rate can reach 2.34 when using SPIHT algorithm decomposition, improving by 14.1% compared with arithmetic coding algorithm. When using 4-level decomposition in integer (9.7) wavelet transform, and lossy compression transmit bit rate is 0.5 bpp, the PSNR of hyper-spectral sequence image can reach around 44.437dB and the image is well reconstructed. The results indicate that the compression project proposed in this thesis has a better effect on the compression of hyper-spectral image