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首页> 外文期刊>International Journal of Robotics & Automation >A SUPERPIXEL-BASED AUTOMATIC CLASSIFICATION METHOD FOR POLARIMETRIC SAR IMAGE
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A SUPERPIXEL-BASED AUTOMATIC CLASSIFICATION METHOD FOR POLARIMETRIC SAR IMAGE

机译：基于Superimetric SAR图像的基于SuperPixel的自动分类方法

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摘要

To improve the classification accuracy of polarimetric synthetic aperture radar (PolSAR) images, a classification of algorithm based on superpixel is proposed, and the locally linear embedding (LLE) dimension reduction algorithm is improved in the process of reducing the feature dimension. The traditional image classification methods are based on pixel, and the classification effect is not satisfactory. The classification method proposed in this paper is based on superpixel segmentation combined with majority voting algorithm and Wishart algorithm. This method is superior to traditional algorithms. The LLE algorithm is improved, and the distance metric combining the Wishart distance with the Euclidean distance is proposed. This method makes the dimension reduce data more favourable for classification. Experimental results of two PolSAR images are presented in this paper. The results show that the proposed method is superior to the traditional method and can achieve better classification effect.
机译：为了提高Polariemetric合成孔径雷达（POLSAR）图像的分类精度，提出了一种基于SuperPixel的算法的分类，并且在减少特征尺寸的过程中提高了局部线性嵌入（LLE）尺寸减少算法。传统的图像分类方法基于像素，并且分类效果不是令人满意的。本文提出的分类方法基于Superpixel分段与多数表决算法和Wishart算法组合。该方法优于传统算法。提出了LLE算法，并且提出了与欧几里德距离相结合的距离度量。该方法使维度降低了对分类更有利的数据。本文提出了两个POLSAR图像的实验结果。结果表明，该方法优于传统方法，可实现更好的分类效果。

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