Main Article Content
Some of these sensors are already available on space-borne devices. Space-borne sensors are currently
acquiring a continual stream of hyperspectral data, and new efficient unsupervised algorithms are required to
analyze the great amount of data produced by these instruments. The identification of image endmembers is a
crucial task in hyperspectral data exploitation. Once the individual endmembers have been identified, several
methods can be used to map their spatial distribution, associations and abundances. This paper reviews the Pixel
Purity Index (PPI), N-FINDR and Automatic Morphological Endmember Extraction (AMEE) algorithms developed
to accomplish the task of finding appropriate image endmembers by applying them to real hyperspectral
data. In order to compare the performance of these methods a metric based on the Root Mean Square Error
(RMSE) between the estimated and reference abundance maps is used.
No Permission Required
Istituto Nazionale di Geofisica e Vulcanologia applies the Creative Commons Attribution License (CCAL) to all works we publish.
Under the CCAL, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, so long as the original authors and source are cited. No permission is required from the authors or the publishers.