Dimensionality Reduction of Hyperspectral Images With Sparse Discriminant Embedding

Abstract

Sparse manifold learning has drawn more and more attentions recently, and sparsity preserving projections (SPP) has been proposed, which inherits the advantages of sparse reconstruction. However, SPP only focuses on the sparse structure, ignoring the discriminant information of labeled samples. In this paper, we proposed a new supervised dimensionality reduction method, which is called sparse discriminant embedding (SDE), for hyperspectral image (HSI) classification. SDE utilizes the merits of both intermanifold structure and sparsity property. It not only preserves the sparse reconstructive relations through $\ell_{1} $ -graph but also enhances the intermanifold separability of data, and the discriminating power of SDE is further improved than SPP. Experiments on two real HSIs collected by the Airborne Visible/Infrared Imaging Spectrometer and Reflective Optics System Imaging Spectrometer sensors are performed to demonstrate the effectiveness of the proposed SDE method.