In hyperspectral image classification, each hyperspectral pixel can be represented by linear combination of a few training samples in the training dictionary. Assuming the training dictionary is available, the hyperspectral pixel can be recovered using a minimal training samples by solving a sparse representation problem, then the weighted coefficients of training samples are obtained and the class of the pixel can be determined, the above process is called classification algorithm based on sparse representation. However, the traditional sparse classification algorithms have not fully utilized the spatial information and classification accuracy is relatively low. In this paper, in order to improve classification accuracy, a new sparse classification algorithm based on First-Order Neighborhood System Weighted (FONSW) constraint is proposed. Compared with other sparse classification algorithms, the experimental results show that the proposed algorithm has a smoother classification map and higher classification accuracy.
Spectral unmixing is an important research hotspot for remote sensing hyperspectral image applications. The unmixing process is comprised of the extraction of spectrally pure signatures (also called endmembers) and the determination of the abundance fractions of endmembers. Due to the inconspicuous signatures of pure spectra and the challenge of inadequate spatial resolution, sparse regression (SR) techniques are adopted in solving the linear spectral unmixing problem. However, the spatial information has not been fully utilized by state-of-art SR-based solutions. In this paper, we propose a new unmixing algorithm which involves in more suitable spatial correlations on sparse unmixing formulation for hyperspectral image. Our algorithm integrates the spectral and spatial information using Adapting Markov Random Fields (AMRF) which is introduced to exploit the spatial-contextual information. Compared with other SR-based linear unmixing methods, the experimental results show that the method proposed in this paper not only improves the characterization of mixed pixels but also obtains better accuracy in hyperspectral image unmixing.