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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Nonlinear Dimensionality Reduction via the ENH-LTSA Method for Hyperspectral Image Classification
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Nonlinear Dimensionality Reduction via the ENH-LTSA Method for Hyperspectral Image Classification

机译：通过ENH-LTSA方法对高光谱图像分类的非线性降维

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摘要

The problems of neglecting spatial features in hyperspectral imagery (HSI) and the high complexity of Local Tangent Space Alignment (LTSA) still exist in the nonlinear dimensionality reduction with LTSA for classification. Therefore, this paper proposes an innovative ENH-LTSA (Enhanced-Local Tangent Space Alignment) method to solve the two problems. First, random projection is introduced to preliminarily reduce the dimension of HSI data. It aims to improve the speed of neighbor searching and the local tangent space construction. Then, the new method presents the similarity measure via the adaptive weighted summation kernel (AWSK) distance. The AWSK distance considers both spectral and spatial features in HSI data, and attempts to ameliorate the k-nearest neighbors (KNNs) of each pixel. Furthermore, the adaptive spatial window is proposed to automatically estimate the proper window size for the description of spatial features. After that, fast approximate KNNs graph construction via Recursive Lanczos Bisection is incorporated into the new method to reduce the complexity of KNNs searching. When finishing constructing each local tangent space, the new method uses a fast low-rank approximate singular value decomposition to speed up eigenvalue decomposition of the global alignment matrix that is constituted with local manifold coordinates. Five groups of experiments with two different HSI datasets are designed to completely analyze and testify the ENH-LTSA method. Experimental results show that ENH-LTSA outperforms LTSA, both in classification results and in computational speed.
机译：在使用LTSA进行分类的非线性降维中，仍然存在忽略高光谱图像（HSI）中的空间特征以及局部切线空间对准（LTSA）的高复杂性的问题。因此，本文提出了一种创新的ENH-LTSA（增强局部切线空间对准）方法来解决这两个问题。首先，引入随机投影以初步减小HSI数据的维数。目的是提高邻居搜索的速度和局部切线空间的构建。然后，新方法通过自适应加权求和核（AWSK）距离提出了相似性度量。 AWSK距离同时考虑了HSI数据中的光谱和空间特征，并尝试改善每个像素的k最近邻（KNN）。此外，提出了自适应空间窗口以自动估计用于描述空间特征的适当窗口大小。之后，将通过递归Lanczos对分的快速近似KNNs图构造合并到新方法中，以降低KNN搜索的复杂性。当完成每个局部切空间的构造时，该新方法使用快速的低秩近似奇异值分解来加快由局部流形坐标构成的全局对齐矩阵的特征值分解。设计了具有两个不同HSI数据集的五组实验，以完全分析和证明ENH-LTSA方法。实验结果表明，ENH-LTSA在分类结果和计算速度上均优于LTSA。

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