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Published in:
2022 | OriginalPaper | Chapter
Application of Improved RBF Neural Network in Remote Sensing Image Restoration
Authors : Yunsen Wang, Yong Wang, Daqiang Feng, Jing Yang, Ke An
Published in: Proceedings of the 7th China High Resolution Earth Observation Conference (CHREOC 2020)
Publisher: Springer Nature Singapore
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Abstract
Neural network is widely used in remote sensing image restoration. To avoid huge time and space consumption of conventional neural network models, a novel remote sensing image restoration algorithm base on improved RBF (Radial Basis Function) neural network is proposed. The training data set could be organized by degrading the high-quality remote sensing images or promoting degraded images’ quality by other necessary methods. And the number of hidden layer is decided by the size of training data at the condition of small training sample scale. To accelerate convergence speed of RBF neural network during training process, conjugate gradient descent method is adopted to realize the weight parameters’ iterative correction. To further reduce calculating time, this paper proposes a matrix factorization algorithm to realize matrix's parallel arithmetic. Simulations and experiments indicate that the improved RBF neural network model could acquire relatively approving remote sensing image restoration results and time overhead.