摘要
针对多源遥感影像分类的需要,本文提出一种光学-极化SAR影像特征融合与分类,利用山东省泰安市区域的Landsat-5与全极化ALOS PALSAR卫星进行了实验验证。实验结果表明,引入全极化SAR目标分解后向散射特征与极化均值纹理特征的特征级影像融合分类,总体精度达到98.804 8%,能够充分利用影像特征之间的合作性与互补性,减少分类结果的椒盐噪声,从而有效地提高影像的分类精度。
To make full use of the multi-source remote sensing images for classification,a new method was proposed based on features fusion and classification of optical and full-polarization Advanced Land Observing Satellite-the Phased Array Type L-band Synthetic Aperture Radar( ALOS PALSAR) images. The Landsat-5 and full-polarization ALOS PALSAR satellite in Taian City of Shandong province are used to verify the results of the experiments. The experiment result shows that the overall accuracy reached to 98.804 8%when the full-polarization SAR target decomposition backscattering features and polarmetric mean texture feature are introduced,which can make full use of the cooperation and complimentarily between the image features,reduce the salt and pepper noise and effectively improve the classification accuracy.
引文
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