Information extraction from the GER 63-channel spectrometer data

Richard K. Kiang

doi:10.1117/12.157048

23 September 1993 Information extraction from the GER 63-channel spectrometer data

Richard K. Kiang

Proceedings Volume 1937, Imaging Spectrometry of the Terrestrial Environment; (1993) https://doi.org/10.1117/12.157048
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

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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