Synthetic Aperture Radar (SAR) image classification is a key technique of SAR image interpretation.
合成孔径雷达(SAR)图像分类是SAR图像解译的关键技术之一。
Aimed at SAR image interpretation, SVM shows good performance in image filtering, image segmentation, target discrimination and classification, as well as polarimetric SAR data classification.
针对SAR图像解译，SVM在图像滤波、图像分割、目标识别与分类、极化数据分类等过程中有很好的处理能力。
The speckle noises of SAR images make the image-interpretation complicated, and deteriorate the effectiveness of classification and information extraction procedures.
SAR图像斑点噪声使图像解译变得复杂，并降低了图像分类和信息提取的有效性。
Speckle noise of Synthetic Aperture Radar (SAR) affects image quality and image interpretation seriously.
合成孔径雷达(SAR)的相干斑噪声严重影响图像质量，降低图像的可判读性。
The inherent speckle noise of SAR image affects the interpretation and the further processing, so it is important to suppress speckle noise of SAR images.
SAR图像固有的斑点噪声严重影响了图像的判读和后续处理，因此抑制SAR图像斑点噪声显得尤其重要。
The inherent speckle noise of SAR image affects the interpretation and the further processing, so it is important to suppress speckle noise of SAR images.
SAR图像固有的斑点噪声严重影响了图像的判读和后续处理，因此抑制SAR图像斑点噪声显得尤其重要。
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