Spectral transforms are widely used for the codification of remote-sensing imagery, with the KarhunenLove transform (KLT) and wavelets being the two most common transforms. The KLT presents a higher coding performance than the wavelets. However, it also carries several disadvantages: high computational cost and memory requirements, difficult implementation, and lack of scalability. In this paper, we introduce a novel transform based on the KLT, which, while obtaining a better coding performance than the wavelets, does not have the mentioned disadvantages of the KLT. Due to its very small amount of side information, the transform can be applied in a line-based scheme, which particularly reduces the transform memory requirements. Extensive experimental results are conducted for the Airborne Visible/Infrared Imaging Spectrometer and Hyperion images, both for lossy and lossless and in combination with various hyperspectral coders. The results of the effects on Reed Xiaoli anomaly detection and k-means clustering are also included. The theoretical and experimental evidences suggest that the proposed transform might be a good replacement for the wavelets as a spectral decorrelator in many of the situations where the KLT is not a suitable option. © 2006 IEEE.
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Publication status||Published - 1 Mar 2011|
- Embedded systems
- Hyperspectral image coding
- KarhuneLove transform (KLT)
- Memory-constrained environments
- Progressive lossy-to-lossless (PLL) and lossy compression