The application of segmented cellular neural networks (SCNNs) for improving noisy satellite imagery performance

In this paper, segmented cellular neural networks (SCNN) have been applied to noisy satellite imagery to improve its performance and appearance. Because of the importance of imagery quality, SCNN has been applied to data for image processing applications that for noise filtering. Multi-level non-linear output capability of SCNN improves image quality. In training recurrent perceptron learning algorithm (RPLA) is used as a learning algorithm. They are applied to noise mounted satellite imagery successfully.