Improving Signal Subspace Identification Using Weighted Graph Structure of Data

Signal subspace identification (SSI) is known as an important preprocessing for most remote sensing processes and its applications. Hence, a graph-based method is presented in this letter to improve identification of signal subspace and its dimension. Our proposed method reduces sensitivity to noise through integrating a weighted graph of image pixels in the cost function of the well-known hyperspectral SSI by minimum error (HySime) method to improve the accuracy of SSI. The method proposed in this letter is a very simple and yet very effective in estimating various types of hyperspectral data. The proposed method was implemented on various synthetic and real data. The results of the experiments on both types of hyperspectral data indicated the accuracy of this approach in estimating the signal subspace as compared with other well-known methods.