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Urban Features Identification from Dual-Pol SAR Images with Filter Properties

机译:城市特色从双极SAR图像识别,具有过滤器属性

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There is no formal definition of feature identification but it depends on the application and context of the problem. This feature acts as primary elements for execution of several algorithms, hence feature identification is one of the significant steps for has been very interesting for several research groups. Various researchers have attempted in this regard for feature identification. The current work presents an approach for urban feature identification from satellite datasets for a detailed analysis of the features for better management of the resources. Several features based feature extraction approach has been attempted to identify the compare with statistical profiling. Microwave remote sensing is one of the significant methods of remote sensing to get the data where our optical sensors usually failed or less capable to provide accurate and timely sensed data. In today’s world, active remote sensing is one of the greatest technologies which is used widely in many application areas. Synthetic aperture radar is the main object to get the actively remote sensed images. Either it’s optical or microwave data, the satellite images has its many errors, in SAR, while receiving the reflected echoes from the target the trouble has occurred in the form of Speckle Noise in an image. In this paper, the focus is on about the Speckle Noise, SLC & GRD data, the filtered images performance with Boxcar and Median filter, degraded and preserving information of an image, reduce speckle noise effect of an image.
机译:没有正式定义特征识别,但这取决于问题的应用和背景。此功能充当执行多个算法的主要元素,因此特征识别是几个研究组非常有趣的重要步骤之一。各种研究人员在这方面尝试了特征鉴定。目前的工作提出了一种来自卫星数据集的城市特征识别方法,以便详细分析,以便更好地管理资源。基于一些特征的特征提取方法已经尝试识别与统计分析的比较。微波遥感是遥感的重要方法之一,以获取我们的光学传感器通常失败或更少能够提供准确和及时感测数据的数据。在今天的世界中,主动遥感是许多应用领域广泛使用的最大技术之一。合成孔径雷达是获得主动遥感图像的主要目标。无论是它的光学还是微波数据,卫星图像都有其许多错误,在SAR中,同时接收来自目标的反射回声,在图像中的斑点噪声的形式发生故障。在本文中,焦点是关于散斑噪声,SLC和GRD数据,通过BoxCar和中值滤波器的滤波图像性能,降级和保存图像的信息,降低了图像的斑点噪声效果。

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