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Object-based speckle filtering using multisensoral remote sensing data

机译:使用多传感器遥感数据的基于对象的斑点滤波

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摘要

Speckle - appearing in SAR Images as random noise - hampers image processing techniques like segmentation and classification. Several algorithms have been developed to suppress the speckle effect. One disadvantage, even with optimized speckle reduction algorithms, is a blurring of the image. This effect, which appears especially along the edges of structures, is leading to further problems in subsequent image interpretation. To prevent a loss of information, the knowledge of structures in the image could be an advantage. Therefore the proposed methodology combines common filtering techniques with results from a segmentation of optical images for an object-based speckle filtering. The performance of the adapted algorithm is compared to those of common speckle filters. The accuracy assessment is based on statistical criteria and visual interpretation of the images. The results show that the efficiency of the speckle filter algorithm can be increased while a loss of information can be reduced using the boundary during the filtering process.
机译:斑点-以随机噪声的形式出现在SAR图像中-妨碍了图像处理技术(如分割和分类)的进行。已经开发了几种算法来抑制斑点效应。即使使用优化的斑点减少算法,一个缺点是图像模糊。特别是沿结构边缘出现的这种效果导致后续图像解释中的其他问题。为了防止信息丢失,了解图像中的结构可能是一个优点。因此,所提出的方法将常见的滤波技术与来自光学图像分割的结果相结合,以用于基于对象的斑点滤波。将自适应算法的性能与普通散斑滤波器的性能进行比较。准确性评估基于统计标准和图像的视觉解释。结果表明,在滤波过程中使用边界可以提高散斑滤波算法的效率,同时可以减少信息丢失。

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