Discriminative Spectral–Spatial Margin-Based Semisupervised Dimensionality Reduction of Hyperspectral Data

The past few years have witnessed prosperity of spectral-spatial processing of hyperspectral images. In this letter, in order to determine the optimal projection subspace of spectrums, we define discriminate spectral-spatial margins (DSSMs) to reveal the local information of hyperspectral pixels and explore the global structures of both labeled and unlabeled data via low-rank representation (LRR). Heterogeneous and homogeneous spectral-spatial neighbors of hyperspectral pixels are used to define DSSMs. By maximizing the DSSM of hyperspectral data and casting an LRR manifold regularizer on finding better projection, both the local and global information of hyperspectral data can be well explored to determine more discriminative features. Some experiments are taken on several real hyperspectral data sets, and the results exhibit its efficiency and superiority to the counterparts, when only a small number of labeled samples are available.