Effect of sensor defects on hyperspectral mixture analysis algorithms

Details Effect of sensor defects on hyperspectral mixture analysis algorithms Effect of sensor defects on hyperspectral mixture analysis algorithms

Oct 1999

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999spie.3753..152w&link_type=abstract

Proc. SPIE Vol. 3753, p. 152-157, Imaging Spectrometry V, Michael R. Descour; Sylvia S. Shen; Eds.

Computer Science

Performance

Scientific paper

Hyperspectral data provides the opportunity to perform a classification of scene data by either deterministic or stochastic techniques. A typical deterministic technique is linear unmixing. This involves finding certain basis spectra called 'end-members' within the scene. Once these spectra are found, the image cube can be unmixed into a map of fractional abundances of each material in each pixel. The N-FINDR algorithm autonomously finds these end-member spectra within the data and then unmixes the scene by determining the fraction of each end-member in each pixel. A stochastic technique for characterizing spectral classes is the Stochastic Expectation Maximization (SEM) approach. This is a spectral clustering technique for classifying spectral terrain data that involves iterative estimation of a Gaussian mixture fit to spectral data. Both techniques can be misled by commonly occurring sensor defects. This is a particular problem with the new class of pushbroom hyperspectral sensors that use a two-dimensional focal plane. These defects are often caused by errors in the calibration process and bad detectors. They manifest themselves in the data as spectrally dependent shading and/or striping and are usually the limit to the performance of the sensor. It is the purpose of this paper to investigate the effect of these sensor defects on the two different classes of algorithms using the N-FINDR and SEM algorithms. Results from actual data are presented.

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