Abstract:

Hyperspectral unmixing estimates the proportions of materials represented within a spectral signature. The over whelming majority of hyperspectral unmixing algorithms are based entirely on the spectral signatures of each individual pixel and do not incorporate the spatial information found in a hyperspectral data cube. In this work, a spectral unmixing algorithm, the Local Information Proportion estimation (LIP) algorithm, is presented. The proposed LIP algorithm incorporates spatial information while determining the proportions of materials found within a spectral signature. Spatial information is incorporated through the addition of a spatial term that regularizes proportion value estimates based on the weighted proportion values of neighboring pixels. Results are shown in the AVIRIS Indian Pines hyperspectral data set.

Links:

Citation:

A. Zare, “Spatial-spectral unmixing using fuzzy local information,” in IEEE Int. Geoscience and Remote Sens. Symposium (IGARSS), 2011, pp. 1139-1142.

@InProceedings{zare2011spatial,
Title = {Spatial-spectral unmixing using fuzzy local information},
Author = {Zare, Alina},
Booktitle = {IEEE Int. Geoscience and Remote Sens. Symposium (IGARSS)},
Year = {2011},
Month = {July},
Pages = {1139-1142},
Doi = {10.1109/IGARSS.2011.6049398},
}