Rectifying airborne scanner measurements using neural networks

Richard K. Kiang

doi:10.1117/12.271513

4 April 1997 Rectifying airborne scanner measurements using neural networks

Richard K. Kiang

Proceedings Volume 3077, Applications and Science of Artificial Neural Networks III; (1997) https://doi.org/10.1117/12.271513
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Richard K. Kiang "Rectifying airborne scanner measurements using neural networks", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); https://doi.org/10.1117/12.271513

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