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Superpixel-based classification of polarimetric synthetic aperture radar images

机译:基于超像素的极化合成孔径雷达图像分类

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摘要

Nowadays, polarimetric synthetic aperture radar (PolSAR) image classification is an important and widely studied topic. To overcome the limitations of pixel-based classification methods, we present, in this paper, a novel superpixel-based classification framework for PolSAR images. The framework takes the spatial relations between pixels into account and fully uses the statistical characteristics and contour information of PolSAR data. The framework is capable of integrating various inherent features of PolSAR data, improving classification accuracies, and making the results more understandable. Experiments on the AIRSAR data set show that the framework provides a promising solution for classifying PolSAR images.
机译:如今,Polariemetric合成孔径雷达(POLSAR)图像分类是一个重要和广泛研究的主题。为了克服基于像素的分类方法的局限,我们在本文中存在于Polsar图像的新型Superpixel的分类框架。该框架考虑了像素之间的空间关系,并充分使用Polsar数据的统计特征和轮廓信息。该框架能够集成Polsar数据的各种固有功能,提高分类精度,使结果更加理解。 Airsar数据集的实验表明该框架为分类Polsar图像提供了有希望的解决方案。

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