提出了分组Karhunen-Leove变换(KLT)和整数小波变换(IWT)的高光谱图像数据压缩方法,并采用整数小波变换技术和Set Partitioning in Hierarchical Trees(SPIHT)压缩编码,实现了对分组Karhun-Loeve变换后的数据压缩。该压缩编码方法与现有压缩方法相比,既保留了Karhun-Loeve变换压缩性能和整数小波变换高压缩比的特点,也宜于实时传输。实验结果表明,分组Karhun-Loeve变换/整数小波变换/SPIHT在相同压缩比下,峰值信噪比比Karhun-Loeve变换/小波变换/WSFCVQ、Karhun-Loeve变换/小波变换/改进的对块零树编码压缩和Karhun-Loeve变换/WT/FSVQ分别提高了6 dB, 9 dB和8 dB,运算时间减少一半,整体压缩性能有了较大的提高。

A method of hyperspectral image data compression using partitioned Karhunen-Loeve transform (PKLT) and integer wavelet transform (IWT) is proposed. IWT technique and set partitioning in hierarchical trees (SPIHT) compression coding are used to compress the PKLT data. In comparison with other methods, PKLT/IWT/SPIHT not only reserves the compression performance of KLT and the performance of IWT's high compression rate, but also is easy to implement real-time transmission. Experimental results show that the peak signal-to-noise ratio(PSNR) of PKLT/IWT/SPIHT improves 6 dB, 9 dB and 8 dB comparing with KLT/WT/WSFCVQ, KLT/WT/IBBZTC and KLT/WT/FSVQ in the condition of same compression rate respectively, computation hours decrease half, and the whole compression performance is enhanced much more.