Preprocessing of EO-1 Hyperion data

Khurshid, K.S.; Stáenz, K.; Sun, Liang; Neville, R. A.; White, H. Peter; Bannari, A.; Champagne, Catherine; Hitchcock, Robert K.

doi:10.5589/m06-014

Cited by 51 publications

(34 citation statements)

References 9 publications

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“…EO1-Hyperion was the first hyperspectral sensor in space with a contiguous spectral bandwidth of 10 nm. However, being designed as a demonstration instrument, this sensor is limited in its signal to noise ratio and calibration accuracy (Datt et al, 2003;Khurshid et al, 2006). The MODIS sensor, such as on AQUA, provides wavebands close to the PRI region (Drolet et al, 2005), however, initial results indicate that the approach at this stage does not sufficiently account for the spatial heterogeneity of the landscape.…”

Section: Landscape Levelmentioning

confidence: 99%

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Hilker

Coops

Wulder

et al. 2008

Science of The Total Environment

255

187

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Section: Landscape Levelmentioning

confidence: 99%

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Hilker

Coops

Wulder

et al. 2008

Science of The Total Environment

255

187

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“…A resampling as part of a georeferencing or an orthorectification alters the image information and should be performed (if necessary) after an atmospheric correction. Similar preprocessing workflows in the context of Hyperion have been published by the authors Staenz et al [30], Khurshid et al [33] and Hitchcock & White [34]. This paper is focused on the evaluation of various destriping techniques as an important part of the Hyperion preprocessing workflow.…”

Section: Hyperion -Used Data Setmentioning

confidence: 99%

“…Extensive approaches for the preprocessing of Hyperion data considering the known distortions were published by an Australian research team belonging to the CSIRO Earth Observation Centre [27][28][29]. Furthermore in [30][31][32][33][34][35], particularly with regard to the correction of image stripes and the smile effect, the authors Datt et al [31] und Goodenough et al [32] made an important contribution.…”

Section: Introductionmentioning

confidence: 99%

Preprocessing of hyperspectral images: a comparative study of destriping algorithms for EO1-hyperion

Scheffler

Karrasch

2013

SPIE Proceedings

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In this study, data from the EO-1 Hyperion instrument were used. Apart from atmospheric influences or topographic effects, the data represent a good choice in order to show different steps of the preprocessing process targeting sensorinternal sources of errors. These include the diffuse sensor noise, the striping effect, the smile effect, the keystone effect and the spatial misalignments between the detector arrays. For this research paper, the authors focus on the striping effect by comparing and evaluating different algorithms, methods and configurations to correct striping errors. The correction of striping effects becomes necessary due to the imprecise calibration of the detector array. This inaccuracy affects especially the first 12 visual and near infrared bands (VNIR) and also a large number of the bands in the short wave infrared array (SWIR). Altogether six destriping techniques were tested on the basis of a Hyperion dataset covering a test site in Central Europe. For the final evaluation, various analyses across all Hyperion channels were performed. The results show that some correction methods have almost no effect on the striping in the images. Other methods may eliminate the striping, but analyses show that these algorithms also alter pixel values in adjacent areas which originally had not been disturbed by the striping effect.

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“…Nevertheless, hyperspectral sensors when compared to multispectral sensors may cause new difficulties in data processing and analysis that could hinder the success of certain research endeavours. One of the challenges in hyperspectral data handling and analysis that requires more attention is the amount of data, as well as high data dimensionality (Khurshid et al, 2006;Tsai et al, 2007). These concerns alone may result in low accuracies when classifying land features.…”

Section: Data Compression Using Principal Components (Pc)mentioning

confidence: 99%

“…Use of hyperspectral imaging potentially gives researchers (Datt et al, 2003;Khurshid et al, 2006;Tsai et al, 2007) the opportunity to accomplish complex analyses that are often difficult with multispectral imaging. One advantage is maintaining a greater amount of spectral bands to define land cover in dense urban regions.…”

Section: Data Compression Using Principal Components (Pc)mentioning

confidence: 99%

Comparing Hyperspectral and Multispectral Imagery for Land Classification of the Lower Don River, Toronto

Ferrato¹,

Forsythe²

2013

JGG

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Urban greenspace is important for the health of cities. Up-to-date databases and information are vital to maintain and monitor growth in cities. During the last decade, advances in spaceborne hyperspectral sensors have resulted in some advantages being gained over multispectral sensors for land cover monitoring (due to increased spectral resolution). The objective of this research was to compare Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 5 Thematic Mapper (TM) and Satellite Probatoire d'Observation de la Terre (SPOT) 5 multispectral data for land cover classification in a dense urban landscape. For comparative analysis, orthorectified aerial imagery provided by the Toronto and Region Conservation Authority (TRCA) was used as ground truth data for accuracy assessment. This study utilized conventional and segmented principal components (CPCA and SPCA) for data compression on the Hyperion imagery, and used principal components analysis (PCA) as a visual enhancement technique for multispectral imagery. Image processing including the generation of the normalized difference vegetation index (NDVI), and mean texture was also performed for both Landsat and SPOT sensors. Unsupervised iterative self-organizing data analysis (ISODATA) classification procedures were performed on all images to produce land cover classification maps for a portion of the Lower Don River in Toronto, Ontario, Canada. Experiments conducted in this research demonstrated that hyperspectral imagery produced a higher overall accuracy (5-6% better) than multispectral data with the same resolution for defining vegetation cover. In addition, SPOT generated greater accuracy results than Landsat or Hyperion for vegetation classes. It was found that conventional Hyperion and segmented Hyperion methods outperformed the Landsat 5 TM sensor for vegetation differences (for tree canopy and open green spaces).

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Preprocessing of EO-1 Hyperion data

Cited by 51 publications

References 9 publications

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Preprocessing of hyperspectral images: a comparative study of destriping algorithms for EO1-hyperion

Comparing Hyperspectral and Multispectral Imagery for Land Classification of the Lower Don River, Toronto

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