On the use of Gaussian synapse ANNs in multi and hyperspectral image data analysis

2003 ◽  
Author(s):  
J.L. Crespo ◽  
R.J. Duro ◽  
F.L. Pena
Keyword(s):  
Data Analysis ◽  
Hyperspectral Image ◽  
Image Data ◽  
Hyperspectral Image Data

scholarly journals Reducing System Artifacts in Hyperspectral Image Data Analysis with the Use of Estimates of the Error Covariance in the Data

2002 ◽  
Author(s):  
DAVID M HAALAND ◽  
MARK H VAN BENTHEM ◽  
CHRISTINE M WEHLBURG ◽  
KOEHLER,IV, FREDERICK W.
Keyword(s):  
Data Analysis ◽  
Hyperspectral Image ◽  
Image Data ◽  
Error Covariance ◽  
Hyperspectral Image Data

Weighting hyperspectral image data for improved multivariate curve resolution results

2008 ◽  
Vol 22 (9) ◽  
pp. 482-490 ◽  
Author(s):  
Howland D. T. Jones ◽  
David M. Haaland ◽  
Michael B. Sinclair ◽  
David K. Melgaard ◽  
Mark H. Van Benthem ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Image Data ◽  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Hyperspectral Image Data

scholarly journals A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Johannes Jordan ◽  
Elli Angelopoulou ◽  
Andreas Maier
Keyword(s):  
Hyperspectral Image ◽  
Imaging Modality ◽  
High Capacity ◽  
Interactive Visualization ◽  
Image Data ◽  
Hyperspectral Image Analysis ◽  
Supervised Segmentation ◽  
Hyperspectral Image Data ◽  
Visualization Techniques ◽  
Application Specific

Multispectral and hyperspectral images are well established in various fields of application like remote sensing, astronomy, and microscopic spectroscopy. In recent years, the availability of new sensor designs, more powerful processors, and high-capacity storage further opened this imaging modality to a wider array of applications like medical diagnosis, agriculture, and cultural heritage. This necessitates new tools that allow general analysis of the image data and are intuitive to users who are new to hyperspectral imaging. We introduce a novel framework that bundles new interactive visualization techniques with powerful algorithms and is accessible through an efficient and intuitive graphical user interface. We visualize the spectral distribution of an image via parallel coordinates with a strong link to traditional visualization techniques, enabling new paradigms in hyperspectral image analysis that focus on interactive raw data exploration. We combine novel methods for supervised segmentation, global clustering, and nonlinear false-color coding to assist in the visual inspection. Our framework coined Gerbil is open source and highly modular, building on established methods and being easily extensible for application-specific needs. It satisfies the need for a general, consistent software framework that tightly integrates analysis algorithms with an intuitive, modern interface to the raw image data and algorithmic results. Gerbil finds its worldwide use in academia and industry alike with several thousand downloads originating from 45 countries.

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