空间一致性邻域保留嵌入的高光谱数据特征提取

局部线性嵌入(LLE)和邻域保留嵌入(NPE)等流形学习方法可以提取高光谱数据的主要结构特征,有助于对数据的理解和进一步处理。但是,这些方法忽视了高光谱图像中相邻像素之间的相关性。针对这个问题,提出一种基于空间一致性思想的邻域保留嵌入(SC-NPE)特征提取算法,通过一个优化的局部线性嵌入,并考虑相邻像素的相关特性,在高维空间建立数据的局部邻域结构。然后寻找一个优化的变换矩阵,将局部邻域结构投影到低维空间,实现数据的特征提取。与LLE和NPE算法相比,SC-NPE既考虑高光谱数据的流形结构,又考虑了其图像域空间信息,可以更好地应用在高光谱数据的特征提取过程中。实验结果表明,SC-NPE特征提取算法在高光谱图像分类方面的性能明显优于其他同类算法。

Hyperspectral data feature extraction using spatial coherence based neighborhood preserving embedding

Manifold learning methods,such as locally linear embedding(LLE) and neighborhood preserving embedding(NPE),extract the main structure feature of hyperspectral images for better understanding and processing of the data.However,these methods ignore the correlation between adjacent pixels in the image.In order to solve this problem,a spatial coherence based neighborhood preserving embedding(SC-NPE) feature extraction algorithm was proposed in this paper.In the proposed algorithm,local neighborhood structure of data was constructed through an optimal local linear embedding in high dimensional space.Meanwhile,the spatial context of pixels was considered by adopting the difference between the surrounding patch of pixels.Then local neighborhood structure of data was projected to the low-dimensional space to perform feature extraction by finding an optimal transformation matrix.Compared with LLE and NPE algorithm,this algorithm took into account not only the manifold structure but also the spatial information of hyperspectral image.As a result,the local neighborhood structure in the proposed algorithm is especially useful in low-dimensional space.The effectiveness of the proposed algorithm is demonstrated in hyperspectral data classification experiments.