NASA Jet Propulsion Laboratory California Institute of Technology Follow this link to skip to the main content

BEACON eSpace at Jet Propulsion Laboratory >
JPL Technical Report Server >
JPL TRS 1992+ >

Please use this identifier to cite or link to this item: http://hdl.handle.net/2014/33284

Title: (abstract) Application of Neural Networks to Hyperspectral Unmixing
Authors: Barhen, Jacob
Toomarian, Nikzad
Issue Date: 10-Oct-1994
Citation: Paris, France
Abstract: The emergence, in recent years, of hyperspectral sensors provides a tremendous opportunity for advancing the process of detailed and direct remote detection and identification from space of targets or surface materials. Such sensors exploit the uniqueness of the corresponding spectral reflectance signatures, which enables high resolution imaging spectrometer data to be processed on a pixel-by-pixel basis. This has implications both for defense-related applications (e.g., surveillance tasks) and in the civilian domain (e.g., for science applications). The purpose of this talk is to discuss a number of strong arguments that support neural networks as a choice for the generalized analysis (e.g., unmixing) of remotely sensed hyperspectral data.
URI: http://hdl.handle.net/2014/33284
Appears in Collections:JPL TRS 1992+

Files in This Item:

File SizeFormat
94-1222.pdf18.18 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, but are furnished with U.S. government purpose use rights.

 

Privacy/Copyright Image Policy Beacon Home Contact Us
NASA Home Page + Div 27
+ JPL Space
Site last updated on December 5, 2014.
If you have any comments or suggestions for this web site, please e-mail Robert Powers.