Multicomponent data have become popular in several scientific fields such as forest monitoring, environmental studies, or sea water temperature detection. Nowadays, this multicomponent data can be collected more than one time per year for the same region. This generates different instances in time of multicomponent data, also called 4D-Data (1D Temporal + 1D Spectral + 2D Spatial). For multicomponent data, it is important to take into account inter-band redundancy to produce a more compact representation of the image by packing the energy into fewer number of bands, thus enabling a higher compression performance. The principal decorrelators used to compact the inter-band correlation redundancy are the Karhunen Loévé Transform (KLT) and Discrete Wavelet Transform (DWT). Because of the Temporal Dimension added, the inter-band redundancy among different multicomponent images is increased. In this paper we analyze the influence of the Temporal Dimension (TD) and the Spectral Dimension (SD) in 4D-Data in terms of coding performance for JPEG2000, because it has support to apply different decorrelation stages and transforms to the components through the different dimensions. We evaluate the influence to perform different decorrelators techniques to the different dimensions. Also we will assess the performance of the two main decorrelation techniques, KLT and DWT. Experimental results are provided, showing rate-distortion performances encoding 4D-Data using KLT and WT techniques to the different dimensions TD and SD.
|Original language||American English|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|Publication status||Published - 2010|
- Multicomponent data
- Remote sensing