19.3 Spectral/Spatial Compression

Since band-to-band correlation is usually very high in hyperspectral imagery, removing such redundant information can achieve a significant compression ratio. Two major approaches are generally used for hyperspectral compression, which are dimensionality reduction by transforms (DRT) and DRBS in Chapter 6. The DR is often accomplished by DRT that compacts information into a small number of components, while the DRBS selects a small number of bands in some sense of optimality to represent data. However, a key issue is how to find an optimal component transform to perform DR for best possible hyperspectral compression or how to effectively select significant bands that can preserve desired information for hyperspectral compression to optimize performance of a designated exploitation application. In other words, the success of DRT and DRBS in hyperspectral compression is determined by how much information is extracted and preserved for the follow-up exploitation data processing after DRT and DRBS. Therefore, DRT and DRBS must be performed by custom-designed criteria for information extraction. This issue can be addressed by hyperspectral compression via dimensionality prioritization in Chapter 20, and by hyperspectral compression via band prioritization in Chapter 21. While the techniques developed in Chapters 20 and 21 can be directly applied to hyperspectral information compression, various versions of DRT and DRBS developed in Chapter 6 are not ...

Get Hyperspectral Data Processing: Algorithm Design and Analysis now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.