基于细胞神经网络的合成孔径雷达图像处理

International Conference on Recent Advances in Space Technologies, 2003. RAST '03. Proceedings of Pub Date : 1900-01-01 DOI:10.1109/RAST.2003.1303925

S. Kent, O. Ucan, T. Ensari

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引用次数: 0

摘要

本文将细胞神经网络(cnn)应用于噪声合成孔径雷达(SAR)图像，以改善其性能和外观。该图像来自土耳其埃尔祖鲁姆。由于成像质量和外观在遥感应用中的重要性，CNN已被应用于图像处理应用中的数据，如噪声滤波和边缘检测。在训练中，使用递归感知器学习算法(RPLA)作为学习算法。根据模板对sar图像进行了测试，取得了满意的效果。

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Synthetic aperture radar image processing using cellular neural networks

In this paper, Cellular Neural Networks (CNNs) have been applied to noisy Synthetic Aperture Radar (SAR) image to improve its performance and appearance. The image has been obtained from Erzurum, Turkey. Because of the importance of imaging quality and appearance for remote sensing applications, CNN has been applied to data for image processing applications that for noise filtering and edge detection. In training, Recurrent Perceptron Learning Algorithm (RPLA) is used as a learning algorithm. According to templates SAR-image has been tested and obtained satisfactory results.

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International Conference on Recent Advances in Space Technologies, 2003. RAST '03. Proceedings of

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