Lossless compression of hyperspectral images

Band ordering and the prediction scheme are the two major aspects of hyperspectral imaging which have been studied to improve the performance of the compression system. In the prediction module, we propose spatio-spectral prediction methods. Two non-linear spectral prediction methods have been proposed in this thesis. NPHI (Non-linear Prediction for Hyperspectral Images) is based on a band look-ahead technique wherein a reference band is included in the prediction of pixels in the current band. The prediction technique estimates the variation between the contexts of the two bands to modify the weights computed in the reference band to predict the pixels in the current band. EPHI (Edge-based Prediction for Hyperspectral Images) is the modified NPHI technique wherein an edge-based analysis is used to classify the pixels into edges and non-edges in order to perform the prediction of the pixel in the current band. Three ordering methods have been proposed in this thesis. The first ordering method computes the local and global features in each band to group the bands. The bands in each group are ordered by estimating the compression ratios achieved between the entire band in the group and then ordering them using Kruskal\u27s algorithm. The other two methods of ordering compute the compression ratios between b-neighbors in performing the band ordering