面向高光谱影像分类的改进局部切空间排列降维

Dimensionality Reduction with Improved Local Tangent Space Alignment for Hyperspectral Imagery Classification

摘要:

提出多策略提升的局部切空间排列算法来解决常规局部切空间排列降维在高光谱影像分类中计算复杂度高的问题.通过引入随机映射来预先减少高光谱影像波段数，降低后续k 邻域和局部切空间构建的计算复杂度；采用递归兰索斯切分算法快速构建近似k 邻域，降低常规k 邻域构建的计算时间；采用快速近似奇异值分解算法提高全局排列矩阵的本征分解计算速度.利用两个不同的高光谱数据集，设计4组实验来分析多策略速度提升的局部切空间排列算法的计算性能和分类效果.实验证明，相比常规局部切空间排列方法，多策略提升的局部切空间排列方法损失约1%左右的总体分类精度却能够提高至少3倍的计算速度.

Abstract:

The paper proposes a new version of local tangent space alignment (LTSA), named multi strategies upgraded local tangent space alignment (MSU LTSA), to solve the problem of computational complexity in dimensionality reduction of hyperspectral imagery (HSI) for classification. First, random projection is introduced into the new method to reduce the number of HSI bands. That decreases the computational complexity of k nearest neighbors (KNNs) construction and local tangent space construction of each pixel. Then, the recursive lanczos bisection algorithm is utilized to construct the fast approximate KNNs graph and it reduces the computational time of regular approach. Finally, when finishing constructing the global alignment matrix, the new method uses the fast approximate singular value decomposition to promote the computational speed of the regular eigenvalue decomposition of global alignment matrix. With two different HSI datasets, four groups of experiments are designed to completely analyze and testify the performance of computation and classification for MSU LTSA. The results show that MSU LTSA speeds up LTSA at least 3 times whereas only degrading about 1% in its overall classification accuracy (OCA).