Tensor subspace analysis for spatial-spectral classification of hyperspectral data

摘要

Remotely sensed data fusion aims to integrate multi-source information generated from different perspectives, acquired with different sensors or captured at different times in order to produce fused data that contains more information than one individual data source. Recently, extended morphological attribute profiles (EMAPs) were proposed to embed contextual information, such as texture, shape, size and etc., into a high dimensional feature space as an alternative data source to hyperspectral image (HSI). Although EMAPs provide greater capabilities in modeling both spatial and spectral information, they lead to an increase in the dimensionality of the extracted features. Conventionally, a data point in high dimensional feature space is represented by a vector. For HSI, this data representation has one obvious shortcoming in that only spectral knowledge is utilized without contextual relationship being exploited. Tensors provide a natural representation for HSI data by incorporating both spatial neighborhood awareness and spectral information. Besides, tensors can be conveniently incorporated into a superpixel-based HSI image processing framework. In our paper, three tensor-based dimensionality reduction (DR) approaches were generalized for high dimensional image with promising results reported. Among the tensor-based DR approaches, the Tensor Locality Preserving Projection (TLPP) algorithm utilized a graph Laplacian to model the pairwise relationship among the tensor data points. It also demonstrated excellent performance for both pixel-wise and superpixel-wise classification on the Pavia University dataset.