中文摘要：

研究了一种将变换矩阵分解为三角元素可逆矩阵（TERM）实现的主成分变换整数近似算法（IPCT）．为限制误差和提高计算效率，改进了TERM分解中选择主元的方法．结合可逆整数PCT和三维Tarp编码技术，提出了一种新的高光谱图像无损压缩算法．该算法在进行空间维小波变换以后，利用改进的IPCT去除谱间相关；在编码阶段，新颖的三维Tarp编码器能利用五个简单的递归滤波器进行概率估计，以驱动一个非自适应算术编码器，对变换系数的显著性映射和细化信息进行熵编码．该算法复杂度较低，能够产生嵌入式码流，并且较已有的算法能获得更高的压缩比．

英文摘要：

Based on the factorization of transform matrix in triangular elementary reversible matrices （ TERM）, an integer approximation algorithm of the principal component transform （IPCT） was proposed. The pivoting method of TERM factorization was improved in order to obtain limited error and enhance computational efficiency. And a new lossless compression algorithm for hyperspectal imagery was developed by combining the perfectly reversible integer PCT with the 3-D Tarp coder. After an integer wavelet transform was applied in spatial domain, the improved IPCT was used as the inter-band decorrelating transform. In coding stage the novel 3-D Tarp coder allows probability estimation with five simple recursive filters. And this probability estimate can be used to drive a non-adaptive arithmetic coder to entropy code significance-map and refinement information of transformed coefficients. The main advantage of our compression algorithm is of low complexity and it can yield embedded bitstreams with higher compression ratios compared with the existing algorithms.