scholarly journals The Performance of Maximum Likelihood, Spectral Angle Mapper, Neural Network and Decision Tree Classifiers in Hyperspectral Image Analysis

2007 ◽  
Vol 3 (6) ◽  
pp. 419-423 ◽  
Author(s):  
Helmi Zulhaidi Mohd Shafri ◽  
Affendi Suhaili ◽  
Shattri Mansor
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Maximum Likelihood ◽  
Decision Tree ◽  
Hyperspectral Image ◽  
Spectral Angle Mapper ◽  
Hyperspectral Image Analysis ◽  
Spectral Angle

Detection and Classification of Norway Spruce Compression Wood in Reflected Light by Means of Hyperspectral Image Analysis

IAWA Journal ◽  
2009 ◽  
Vol 30 (1) ◽  
pp. 59-70 ◽  
Author(s):  
Philipp Duncker ◽  
Heinrich Spiecker
Keyword(s):  
Image Analysis ◽  
Norway Spruce ◽  
Tree Ring ◽  
Compression Wood ◽  
Hyperspectral Image ◽  
Normal Wood ◽  
Cross Sectional ◽  
Spectral Angle Mapper ◽  
Hyperspectral Image Analysis ◽  
Reflected Light

A methodology has been developed based on reflected light to detect compression wood in stem cross sections of Norway spruce (Picea abies [L.] Karst.). In addition to quantify the spatial distribution of compression wood, the chronological pattern of its formation is recorded by cross linking the pixel classification to the tree ring sequence. An imaging spectrometer is used to record the spectral characteristics in the visible light and near infrared of the cross-sectional surface. Cross-sectional areas are classified by hyperspectral image analysis into severe compression wood, moderate compression wood, normal wood, and background/cracks. The classification is performed by the Spectral Angle Mapper algorithm, which compares the standardized spectrum of each pixel with reference spectra stored in a spectral library. The reference spectra are obtained from selected training areas of the different compression wood severity classes identified by cell characteristics under a light microscope. The tree ring boundaries are located in a grey scale image which shows the spatial information at wavelength 435 nm and the annual radial increment is measured. The classification accuracy is tested by a confusion matrix and cross-analysed with High-Frequency Densitometry.

scholarly journals Trends in Deep Learning for Medical Hyperspectral Image Analysis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Uzair Khan ◽  
Sidike Paheding ◽  
Colin Elkin ◽  
Vijay Devabhaktuni
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Image Analysis

scholarly journals Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping

2021 ◽  
Vol 13 (6) ◽  
pp. 1178
Author(s):  
Jordi Cristóbal ◽  
Patrick Graham ◽  
Anupma Prakash ◽  
Marcel Buchhorn ◽  
Rudi Gens ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Pilot Study ◽  
Data Processing ◽  
High Latitude ◽  
Hyperspectral Images ◽  
The Arctic ◽  
Wetland Mapping ◽  
Kappa Index ◽  
Spectral Angle Mapper ◽  
Spectral Angle

A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, and speed to minimize the strong bidirectional reflectance distribution function (BRDF) effects present at high latitudes and establishing improved data processing workflows for the high-latitude environments. Hyperspectral images were acquired on two clear-sky days in early September, 2018, over three pilot study areas that together represented a wide variety of vegetation and wetland environments. Steps to further minimize BRDF effects and achieve a higher geometric accuracy were added to adapt and improve the Hyspex data processing workflow, developed by the German Aerospace Center (DLR), for high-latitude environments. One-meter spatial resolution hyperspectral images, that included a subset of only 120 selected spectral bands, were used for wetland mapping. A six-category legend was established based on previous U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service (USFWS) information and maps, and three different classification methods—hybrid classification, spectral angle mapper, and maximum likelihood—were used at two selected sites. The best classification performance occurred when using the maximum likelihood classifier with an averaged Kappa index of 0.95; followed by the spectral angle mapper (SAM) classifier with a Kappa index of 0.62; and, lastly, by the hybrid classifier showing lower performance with a Kappa index of 0.51. Recommendations for improvements of future work include the concurrent acquisition of LiDAR or RGB photo-derived digital surface models as well as detailed spectra collection for Alaska wetland cover to improve classification efforts.

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