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Coastline extraction from SAR COSMO-SkyMed data using a new neural network algorithm

机译:使用新的神经网络算法从SAR Cosmo-Skymed数据中提取海岸线

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The mapping of the coastline is a well known and tested procedure exploiting the capabilities of optical satellite sensor. Nevertheless, it is affected by several inherent limits like weather condition and revisit time of the areas. The recent availability of very high-resolution X-Band SAR data acquired by a constellation of satellites with frequent revisit capabilities has brought a potential alternative, or support, for this kind of application. To this purpose a new automatic algorithm, based on Pulse Coupled Neural Networks, has been developed to process COSMO-SkyMed products taken with different polarization, geometric configuration and measurements mode. The results have been validated through a GPS survey, also respect to a traditional C-band technique applied on X-band, with the final intent of an assessment of the real impact of the proposed procedure in the coastal mapping application.
机译:海岸线的映射是一种众所周知的和测试的程序,利用光学卫星传感器的能力。然而,它受到几个固有限制的影响,如天气状况和地区的重新审视时间。近期由具有频繁Revisit功能的卫星星座获得的非常高分辨率的X频段SAR数据,为此应用提供了潜在的替代或支持。为此目的,已经开发了一种基于脉冲耦合神经网络的新型自动算法,以处理具有不同极化,几何配置和测量模式的彩色碳化产品。通过GPS调查验证了结果,也尊重X频段的传统C波段技术,最终意图评估拟议程序在沿海绘图申请中的实际影响。

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