The availability of hyperspectral images has increased in recent years, which is used in military and civilian applications,
such as target recognition, surveillance, geological mapping and environmental monitoring. Because of its abundant data
quantity and special importance, now it exists lossless compression methods of hyperspectral images mainly exploiting
the strong spatial or spectral correlation. C-DPCM-APL is a method that achieves highest lossless compression ratio on
the CCSDS hyperspectral images acquired in 2006 but consuming longest processing time among existing lossless
compression methods to determine the optimal prediction length for each band. C-DPCM-APL gets best compression
performance mainly via using optimal prediction length but ignoring the correlationship between reference bands and the
current band which is a crucial factor that influences the precision of prediction. Considering this, we propose a method
that selects reference bands according to the atmospheric absorption characteristic of hyperspectral images. Experiments
on CCSDS 2006 images data set show that the proposed reduces the computation complexity heavily without decaying
its lossless compression performance when compared to C-DPCM-APL.
By fully exploiting the high correlation of the pixels along an edge, a new lossless compression algorithm for hyperspectral images using adaptive edge-based prediction is presented in order to improve compression performance. The proposed algorithm contains three modes in prediction: intraband prediction, interband prediction, and no prediction. An improved median predictor (IMP) with diagonal edge detection is adopted in the intraband mode. And in the interband mode, an adaptive edge-based predictor (AEP) is utilized to exploit the spectral redundancy. The AEP, which is driven by the strong interband structural similarity, applies an edge detection first to the reference band, and performs a local edge analysis to adaptively determine the optimal prediction context of the pixel to be predicted in the current band, and then calculates the prediction coefficients by least-squares optimization. After intra/inter prediction, all predicted residuals are finally entropy coded. For a band with no prediction mode, all the pixels are directly entropy coded. Experimental results show that the proposed algorithm improves the lossless compression ratio for both standard AVIRIS 1997 hyperspectral images and the newer CCSDS test images.