Dimensionality reduction for hyperspectral imagery manifold learning based on spectral gradient angles
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摘要: 为了挖掘高光谱数据的光谱局部特征，从高光谱遥感数据内在的非线性结构出发，提出了一种基于光谱梯度角的高光谱影像流形学习降维方法。采用局部化流形学习算法局部保持投影（LPP）对高光谱遥感数据进行非线性降维，对距离度量进行改进，将能够更好刻画高光谱影像光谱局部特征的光谱梯度角相似性度量应用于LPP方法，并用真实高光谱图像进行降维实验，取得了优于LPP方法和采用光谱角的LPP方法的结果。结果表明，在光谱规范化特征值方面，所提方法优于LPP方法和采用光谱角的LPP方法；在信息量的保持方面，具有更好的局部细节信息保持量。采用光谱梯度角的流形学习方法用于高光谱影像降维能取得较好的降维效果。Abstract: In order to extract the local characteristics of hyperspectral data, a dimensionality reduction method of hyperspectral imagery manifold learning based on spectral gradient angle was proposed from the nonlinear structure of hyperspectral imagery. Locality preserving projection (LPP) of localized manifold learning algorithm was performed to reduce the dimensionality of hyperspectral remote sensing data. In order to improve the distance metric, similarity measurement of spectral gradient angle, which can better characterize local features of hyperspectral images, was applied to LPP method. The real hyperspectral images were subjected to dimensionality reduction experiments.The results were better than the original LPP method and the LPP method using the spectral angle. The results show that the proposed method is superior to LPP method and LPP method using the spectral angle in the spectral normalized eigenvalues. Meanwhile, the proposed method can also obtain a good performance in information retainment and have better local information retention. Therefore, the manifold learning method with spectral gradient angle has a better performance in dimensionality reduction of hyperspectral images.
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Figure 3. Dimensionality reduction results of data 1 by three algorithms
a—LPP algorithm b—SA-LPP algorithm c—SGA-LPP algorithm
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Figure 4. Dimensionality reduction results of data 2 by three algorithms
a—LPP algorithm b—SA-LPP algorithm c—SGA-LPP algorithm
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Figure 5. Comparison of the normalized eigenspectra by LPP, SA-LPP and SGA-LPP
a—data 1 b—data 2
Table 1 Index evaluation of dimension reduction for data 1
algorithms RRI MSE EI LPP 89.35% 1.7637×106 4.54 SA-LPP 92.41% 1.4554×106 6.33 SGA-LPP 94.54% 1.5469×106 6.98 
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Table 2 Index evaluation of dimension reduction for data 2
algorithms RRI MSE EI LPP 88.42% 2.4735×106 5.24 SA-LPP 90.87% 1.8373×106 7.85 SGA-LPP 93.19% 1.9724×106 8.20
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