Multi-view feature extraction for hyperspectral image classification

We study the multi-view feature extraction (MV-FE) framework for the classification of hyperspectral images acquired from airborne and spaceborne sensors. This type of data is naturally composed by distinct blocks of spectral channels, forming the hypercube. To reduce the dimensionality of the data by taking advantage of this particular structure, an unsupervised multi-view feature extraction method is applied prior to classification. First, a technique to automatically obtain the blocks, based on the global spectral correlation matrix, is applied. Then, the kernel canonical correlation analysis is performed in a multi-view setting (MVkCCA) to find projections of the data blocks in a correlated subspace, gaining thus discriminant power. Experiments using the linear discriminant classifier (LDA) show the appropriateness of adopting a MV-FE approach.