Paper
23 September 1993 Information extraction from the GER 63-channel spectrometer data
Author Affiliations +
Abstract
The unprecedented data volume in the era of NASA's Mission to Planet Earth (MTPE) demands innovative information extraction methods and advanced processing techniques. The neural network techniques, which are intrinsic to distributed parallel processings and have shown promising results in analyzing remotely sensed data, could become the essential tools in the MTPE era. To evaluate the information content of data with higher dimension and the usefulness of neural networks in analyzing them, measurements from the GER 63-channel airborne imaging spectrometer data over Cuprite, Nevada, are used. The data are classified with 3-layer Perceptron of various architectures. It is shown that the neural network can achieve a level of performance similar to conventional methods, without the need for an explicit feature extraction step.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Richard K. Kiang "Information extraction from the GER 63-channel spectrometer data", Proc. SPIE 1937, Imaging Spectrometry of the Terrestrial Environment, (23 September 1993); https://doi.org/10.1117/12.157048
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KEYWORDS
Neural networks

Spectroscopy

Remote sensing

Atmospheric sensing

Sensors

Information theory

Data centers

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