THE REMOTE SENSING IMAGE GEOMETRICAL MODEL OF BP NEURAL NETWORK
- 1Beijing Institute of Space Mechanics & Electricity, Beijing, China
- 2Beijing Key Laboratory of Advanced Optical Remote Sensing Technology, Beijing, China
- 3Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- 4Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of P. R. China, Beijing, China
Keywords: Imagery geometry model (IGM), Back propagation (BP) neural network, Rigorous sensor models (RSM), Generalized sensor models, Rational polynomial coefficients (RPCs)
Abstract. Imagery geometry models (IGMs) of the high-resolution satellite images (HRSIs) are always of great interest in the photogrammetry and remote sensing community for the raising new kinds of sensors and imaging systems. Especially the generalized sensor models (GSMs) have been widely used for positioning of satellite images, and the accuracy are already validated. Since Back propagation (BP) neural network is a better choice for the two key reasons of the replacement of physical sensor models by generalized sensor models, numerous mathematical estimations for every specialized sensor, and secret equations of the IGMs. Experiments are carried out to test the approximation accuracy of the new generalized sensor model. And the experimental results show that, the BP neural network is of extremely high accuracy for satellite imagery photogrammetric restitution.