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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Semisupervised Classification of Remote Sensing Images With Hierarchical Spatial Similarity
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Semisupervised Classification of Remote Sensing Images With Hierarchical Spatial Similarity

机译:具有分级空间相似性的遥感影像的半监督分类

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摘要

A semisupervised kernel deformation function, including spatial similarity, is proposed for the classification of remote sensing (RS) images. The method exploits the characteristic of these images, in which spatially nearby points are likely to belong to the same class. To fulfill this assumption, a kernel encoding both spatial and spectral proximity using unlabeled samples is proposed. In this letter, two similarity functions for constructing a spatial kernel are proposed. Experimental tests are performed on very high-resolution multispectral and hyperspectral data. With respect to state-of-the-art semisupervised methods for RS images, the proposed method incorporating spatial similarity obtains higher classification accuracy values and smoother classification maps.
机译:提出了一种包括空间相似度的半监督核变形函数,用于遥感图像的分类。该方法利用了这些图像的特征,其中空间上邻近的点可能属于同一类。为了满足这个假设,提出了使用未标记样本对空间和频谱接近度进行编码的内核。在这封信中,提出了两个用于构造空间核的相似性函数。实验测试是在非常高分辨率的多光谱和高光谱数据上进行的。对于RS图像的最新半监督方法,该方法结合了空间相似性,可获得更高的分类精度值和更平滑的分类图。

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