Improving hyperspectral image classification using smoothing filter via sparse gradient minimization

In hyperspectral imagery, there exist homogeneous regions where neighboring pixels tend to belong to the same class with high probability. However, even though neighboring pixels are from the same material, their spectral characteristics may be different due to various factors, such as internal instrument noise or atmospheric scattering, which results in misclassification. In this work, the proposed framework employs a smoothing filter based on sparse gradient minimization, which is expected to eliminate the inherent variations within a small neighborhood. Experimental results for two hyperspectral image datasets demonstrate that the proposed algorithm significantly improve classification accuracy.