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Proceedings Paper

Rectifying airborne scanner measurements using neural networks
Author(s): Richard K. Kiang
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Paper Abstract

Popularized by the images from weather satellites and other Earth observing satellites, remote sensing from space has already become a household term. Airborne remote sensing, however, still holds its important place in the development of the remote sensing technology and in many applications. Prototype, proof-of-concept instruments are flown on aircraft before their improved versions are deployed on space shuttles or satellites. Airborne remote sensing is also more practical for regional applications. Since an aircraft flies in the Earth's atmosphere, factors contributing to geometric distortion are less systematic and more random. Substantial amount of effort is usually required to rectify the measurements. In this study, a scanner model is developed to generate simulated aircraft measurements. A backpropagation network and other variations are used to map the measurement space to the physical space. For measurements conducted over extensive area, techniques of anchoring the training data is developed such that geometric rectification can be performed in segments. Advantages of the neural network methods over the traditional method, and the need of constrained optimization are discussed.

Paper Details

Date Published: 4 April 1997
PDF: 10 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271513
Show Author Affiliations
Richard K. Kiang, NASA Goddard Space Flight Ctr. (United States)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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