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Improved SLIC superpixel generation algorithm and its application in polarimetric SAR images classification

机译：改进的SLIC超像素生成算法及其在极化SAR图像分类中的应用

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In recent years, more attention has been attracted on the classification of polarimetric SAR (PolSAR) images and a lot of methods have been proposed. With the resolution increasing, the pixel-based classification methods reveal insufficiency, therefore, this paper proposes an improved SLIC superpixel algorithm for PolSAR images classification. Firstly, effective polarization features, such as polarimetric scattering power of typical scattering mechanism based on target decomposition and spatial texture information based on statistical analysis, are extracted from PolSAR images and these features construct a feature vector to obtain a better description of PolSAR images. Then, the Euclidean distance of the feature vector is used to improve the SLIC algorithm to obtain superpixels segmentation and meantime reduce the execution time. In addition, a useful dissimilarity measurement is implemented to maintain the edges of different area in PolSAR image. At last, based on the superpixel segmentation result, a region-based classification using SVM is conducted. The proposed method is validated by EMISAR test PolSAR image and the experimental results confirm the performance and potential of the proposed method in PolSAR image interpretation.
机译：近年来，极化SAR（PolSAR）图像的分类引起了更多关注，并提出了许多方法。随着分辨率的提高，基于像素的分类方法显示出不足，因此，本文提出了一种改进的SLIC超像素算法用于PolSAR图像分类。首先，从PolSAR图像中提取有效的偏振特征，例如基于目标分解的典型散射机制的偏振散射能力以及基于统计分析的空间纹理信息，这些特征构成特征向量以获得对PolSAR图像的更好描述。然后，利用特征向量的欧几里得距离来改进SLIC算法以获得超像素分割，并减少执行时间。另外，实现了有用的相异性测量以维持PolSAR图像中不同区域的边缘。最后，基于超像素分割结果，使用SVM进行基于区域的分类。通过EMISAR测试PolSAR图像验证了该方法的有效性，实验结果验证了该方法在PolSAR图像解释中的性能和潜力。

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