Oriented the application background of target recognition and classification for hyperspectral imagery,a new target segmentation method for hyperspectral imagery based on fast independent component analysis (FastICA) is proposed.The concept of virtual dimensionality was introduced to determine the number of target endmembers.The mixing matrix of FastICA was initialized by anomaly endmembers,which were extracted from hyperspectral imagery by using unsupervised orthogonal subspace projection.Minimum noise fraction was employed for dimensionality reduction of original data volumes,and FastICA transform was performed on the selected principal components with high signal-noise ratio (SNR) to generate independent components.Finally,constant false alarm rate (CFAR) detection was performed on each IC,which was followed by morphologic filtering.Experimental results on AVIRIS data show that the proposed algorithm can give better target detection performance,as well as better target segmentation.