Semi-supervised sparse dimensionality reduction for hyperspectral image classification

In this paper, a semi-supervised dimensionality reduction method based on sparse representation is proposed. The s-parse representation has the ability to distinguish samples, exactly, the sparse coefficient in sparse representation will be large when the represented samples and the entries of the dictionary are in the same class, but it will be small if they are not. So we use the sparse coefficient to construct our l
₁
-graph in which the connection weight is defined as the contribution of each entries in the dictionary when construct this sample. Considering the goal that reduce the dimension of each pixel and preserve the discriminative information as much as possible we propose an regularization term incorporating the structures reflected by l
₁
-graph. Although labeled samples are always insufficient in HSI processing, we make full use of them with abundant unlabeled samples to improve the performance after dimensionality reduction. So we then introduce LDA method and extend it as a semi-supervised method. Finally we evaluate our method in practical classification work and experimental results on real hyperspectral image show that the proposed method can achieve better and more stable results.