An efficient approach for multi-temporal hyperspectral images interpretation based on high-order tensor

The main purpose of this paper is to propose and to validate a new multi-temporal algorithm for hyperspectral endmembers extraction. The advanced approach is based on multi-linear algebra, spectral analysis and tensor data structure for each pixel. The detection of an endmember in the time series is done by the interpretation of the spatial-temporal signature in a multi-dimensional tonsorial space. Thus, the images could have different resolutions and could be coming from different dates. A multi-temporal synthetic and Hyperion series images were used to assess the effectiveness of the proposed algorithm. The obtained results show good performances with both permanent and temporal known features.