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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Superpixel-Based Classification With an Adaptive Number of Classes for Polarimetric SAR Images
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Superpixel-Based Classification With an Adaptive Number of Classes for Polarimetric SAR Images

机译：基于超像素的极化SAR图像的自适应分类

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摘要

Polarimetric synthetic aperture radar (PolSAR) image classification, an important technique in the remote sensing area, has been deeply studied for a couple of decades. In order to develop a robust automatic or semiautomatic classification system for PolSAR images, two important problems should be addressed: 1) incorporation of spatial relations between pixels; 2) estimation of the number of classes in the image. Therefore, in this paper, we present a novel superpixel-based classification framework with an adaptive number of classes for PolSAR images. The approach is mainly composed of three operations. First, the PolSAR image is partitioned into superpixels, which are local, coherent regions and preserve most of the characteristics necessary for image information extraction. Then, the number of classes and each class center within the data are estimated using the pairwise dissimilarity information between superpixels, followed by the final classification operation. The proposed framework takes the spatial relations between pixels into consideration and makes good use of the inherent statistical characteristics and contour information of PolSAR data. The framework is capable of improving the classification accuracy, making the results more understandable and easier for further analyses, and providing robust performance under various numbers of classes. The performance of the proposed classification framework on one synthetic and three real data sets is presented and analyzed; and the experimental results show that the framework provides a promising solution for unsupervised classification of PolSAR images.
机译：极化合成孔径雷达（PolSAR）图像分类是遥感领域的一项重要技术，已经进行了数十年的深入研究。为了开发用于PolSAR图像的强大的自动或半自动分类系统，应解决两个重要问题：1）合并像素之间的空间关系； 2）合并像素之间的空间关系。 2）估计图像中的类数。因此，在本文中，我们提出了一种新颖的基于超像素的分类框架，该分类框架具有针对PolSAR图像的自适应数量的类别。该方法主要由三个操作组成。首先，PolSAR图像被划分为多个超像素，这些超像素是局部的，相干的区域，并保留了图像信息提取所需的大多数特征。然后，使用超像素之间的成对差异信息估计数据中的类数和每个类中心，然后进行最终分类操作。提出的框架考虑了像素之间的空间关系，并充分利用了PolSAR数据的固有统计特征和轮廓信息。该框架能够提高分类的准确性，使结果更易于理解且更易于进一步分析，并且在各种类别的情况下都具有出色的性能。提出并分析了建议的分类框架在一个综合数据集和三个真实数据集上的性能；实验结果表明，该框架为PolSAR图像的无监督分类提供了有希望的解决方案。

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