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Adaptive Locality Preserving Projection for Hyperspectral Image Classification

机译:高光谱图像分类的自适应位置保存投影

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In hyperspectral image classification, small number of labeled samples versus high dimensional data is one of majorchallenges. Semi-supervised learning has shown potential to relief the dilemma. Compared with its supervised learningcounterpart, semi-supervised learning exploits both intrinsic structure of labeled and unlabeled samples. In this work, weproposed a graph fusion based semi-supervised learning method for hyperspectral image classification. More specially,two graphs are constructed from spectral-spatial Gabor features and original spectral signatures, respectively, and then areintegrated using an affine combination. Experimental results from an AVIRIS hyperspectral dataset verify the excellentclassification performance of our method.
机译:在高光谱图像分类中,少量标记的样本与高维数据是主要的一个挑战。半监督学习表明潜力可以缓解困境。与其监督学习相比对应,半监督学习利用标记和未标记样品的内在结构。在这项工作中,我们提出了一种基于曲线融合的高光谱图像分类的半监督学习方法。更特别,从频谱空间Gabor特征和原始光谱签名构建两个图形,然后是使用仿射组合集成。 Aviris Hyperspectral DataSet的实验结果验证了优秀的我们方法的分类性能。

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