Abstract
During the last years, several high-resolution sensors have been developed for hyperspectral remote sensing applications. 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.
Highlights
IntroductionThe use of hyperspectral imaging sensor data to study the Earth’s surface is based on the capability of such sensors to provide hundreds of spectral bands (high resolution spectra), providing one spectrum per pixel, along with the image data
Imaging spectroscopy is the measurement and analysis of spectra acquired as images
One of the most successful approaches has been the Pixel Purity Index or PPI (Boardman et al, 1995), which is based on the geometry of convex sets (Ifarraguerri and Chang, 1999)
Summary
The use of hyperspectral imaging sensor data to study the Earth’s surface is based on the capability of such sensors to provide hundreds of spectral bands (high resolution spectra), providing one spectrum per pixel, along with the image data. The imaging spectroscopy concept can be considered in the general problem of solving unknowns from measurements. The hyperspectral signature collected by the sensor at each pixel is formed by an integration of signatures, associated with the purest portions of the sub-scene. These signatures, which can be considered macroscopically pure, are usually named «endmembers» in hyperspectral analysis terminology (Bateson et al, 2000).
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