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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Classification of Polarimetric SAR Images Using Multilayer Autoencoders and Superpixels
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Classification of Polarimetric SAR Images Using Multilayer Autoencoders and Superpixels

机译：使用多层自动编码器和超像素对极化SAR图像进行分类

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摘要

A new polarimetric synthetic aperture radar (PolSAR) images classification method based on multilayer autoencoders and superpixels is proposed in this paper. First, in order to explore the spatial relations between pixels in PolSAR data, the RGB image formed with Pauli decomposition is used to produce superpixels to integrate contextual information of neighborhood. Second, multilayer autoencoders network is used to learning the features used for distinguishing the multiple categories for each pixel, and a softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. Finally, the probability distributions is regarded as a new probabilistic metric and introduced to k-nearest neighbor to improve the accuracy of classification based on superpixels, which takes spatial relationship between pixels into consideration, and it is robust to speckle noise. The proposed method makes good use of the scattering characteristics in each pixel and spatial information of PolSAR data. Compared with other state-of-the-art methods, the results of proposed method show better agreement with the ground truth and significant improvement in classification accuracy and discriminability of small differences between different categories.
机译：提出了一种基于多层自动编码器和超像素的极化合成孔径雷达图像分类方法。首先，为了探究PolSAR数据中像素之间的空间关系，使用Pauli分解形成的RGB图像产生超像素，以整合邻域的上下文信息。其次，多层自动编码器网络用于学习用于区分每个像素的多个类别的特征，并且应用softmax回归以生成每个像素所有类的预测概率分布。最后，概率分布被认为是一种新的概率度量，并被引入到k近邻中，以提高基于超像素的分类精度，该概率分布考虑了像素之间的空间关系，并且对斑点噪声具有鲁棒性。所提出的方法充分利用了每个像素的散射特性和PolSAR数据的空间信息。与其他最新方法相比，该方法的结果与地面实况具有更好的一致性，并且在分类准确性和区分不同类别之间的细微差别方面具有显着提高。

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