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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.
Appears in Collections:JPL TRS 1992+

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