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Segmented PCA and JPEG2000 for hyperspectral image compression

Principal component analysis (PCA) is the most efficient spectral decorrelation approach for hyperspectral imagery. In conjunction with JPEG2000 for optimal bit allocation and spatial coding, the resulting PCA+JPEG2000 can yield superior rate-distortion performance and the following data analysis performance. However, the involved overhead bits consumed by the large transformation matrix may affect the performance at low bitrates, particularly when the image spatial size is relatively small compared to the spectral dimension. In this paper, we propose to apply the segmented principal component analysis (SPCA) to mitigate this effect. The resulting SPCA+JPEG200 may improve the compression performance even when PCA+JPEG2000 is applicable.