Some of the artefacts are hard to remove, however quite usefull preprocessing of the images could be done via morphological reconstruction (www.vincent-net.com/luc/papers/92cvpr_recons.pdf). It is very simple, however little bit time consuming. For large images it is better to do picewise reconstruction, because of memory burden.

Some of the artefacts are hard to remove, however quite usefull preprocessing of the images could be done via morphological reconstruction (www.vincent-net.com/luc/papers/92cvpr_recons.pdf). It is very simple, however little bit time consuming. For large images it is better to do picewise reconstruction, because of memory burden.

Artifacts in the images may due to movements between camera and observer. It can not be completely eliminated on the images. However it can be minimized Via per-processing only.

Hello Salari, these points may helpful to you 

Many algorithms have been proposed to automatically find spectral endmembers in hyperspectral data sets. Perhaps one of the most popular ones is the pixel purity index (PPI), available in the ENVI software from Exelis Visual Information Solutions. Although the algorithm has been widely used in the spectral unmixing community, it is highly time consuming as its precision increases asymptotically. Due to its high computational complexity, the PPI algorithm has been recently implemented in several high performance computing architectures including COMMODITY clusters, heterogeneous and distributed systems, field programmable gate arrays (FPGAs) and graphics processing units (GPUs).