ACCOUNTING FOR VARIANCE IN HYPERSPECTRAL DATA COMING FROM LIMITATIONS OF THE IMAGING SYSTEM
Over the course of the past few years, a number of methods was developed to incorporate hyperspectral imaging specifics into generic data mining techniques, traditionally used for hyperspectral data processing. Projection pursuit methods embody the largest class of methods empoyed for hyperspectra...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/365/2016/isprs-archives-XLI-B7-365-2016.pdf |
| Summary: | Over the course of the past few years, a number of methods was developed to incorporate hyperspectral imaging specifics into generic
data mining techniques, traditionally used for hyperspectral data processing. Projection pursuit methods embody the largest class of
methods empoyed for hyperspectral image data reduction, however, they all have certain drawbacks making them either hard to use or
inefficient. It has been shown that hyperspectral image (HSI) statistics tend to display “heavy tails” (Manolakis2003)(Theiler2005), rendering
most of the projection pursuit methods hard to use. Taking into consideration the magnitude of described deviations of observed
data PDFs from normal distribution, it is apparent that <i>a priori</i> knowledge of variance in data caused by the imaging system is to be
employed in order to efficiently classify objects on HSIs (Kerr, 2015), especially in cases of wildly varying SNR. A number of attempts
to describe this variance and compensating techniques has been made (Aiazzi2006), however, new data quality standards are not yet
set and accounting for the detector response is made under large set of assumptions. Current paper addresses the issue of hyperspectral
image classification in the context of different variance sources based on the knowledge of calibration curves (both spectral and radiometric)
obtained for each pixel of imaging camera. A camera produced by ZAO NPO Lepton (Russia) was calibrated and used to obtain
a test image. <i>A priori</i> known values of SNR and spectral channel cross-correlation were incorporated into calculating test statistics used
in dimensionality reduction and feature extraction. Expectation-Maximization classification algorithm modification for non-Gaussian
model as described by (Veracini2010) was further employed. The impact of calibration data coarsening by ignoring non-uniformities
on false alarm rate was studied. Case study shows both regions of scene-dominated variance and sensor-dominated variance, leading to
different preprocession parameters and, ultimatively, classification results. A multilevel system for denoting hyperspectral pushbroom
scanners calibration quality was proposed. |
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| ISSN: | 1682-1750 2194-9034 |