Abstract: Efficient compression for hyperspectral imagery has been the research focus for the developmentof remote sensing technique. The small targets information protection during the compression process without anypreknowledge should be necessarily considered. This paper presents a new lossy compression method forhyperspectral imagery based on fast independent component analysis (FastICA). Virtual dimensionality isintroduced to determine the number of target endmembers. The mixing matrix of FastICA is initialized by targetendmembers. Minimum noise fraction is employed for dimensionality reduction of original data volumes, andFastICA is performed on the selected principal components to generate independent components. Then, constantfalse alarm rate detection is performed on each IC, which is followed by morphologic filtering. Karhunen-Loevetransform is used to decorrelate the spectral redundancy, general scaling-based method is selected to upshift thewavelet coefficients of interested targets. Finally, each principle component is allocated optimal rate andcompressed by SPIHT algorithm. Experimental results on AVIRIS data show that the proposed method not onlyprovides high compression performance, but also preserves targets interested effectively.