Laplacian matrix graph for anomaly target detection in hyperspectral images. Issue 8 (23rd February 2022) - Zhang, Fenggan; He, Fang; Hu, Haojie
Abstract: To improve the accuracy of abnormal target detection in hyperspectral images, an abnormal target detection method based on Laplacian matrix graph (LGD) is proposed. The method makes full use of the spatial and spectral information of hyperspectral abnormal targets by constructing the full‐connection graph and the nearest neighbour matrix obtained by the Gaussian kernel function. In the graph, the total variation of the graph signal calculated by the Laplacian matrix is taken as the evaluation function to judge the abnormal target, so as to realize the detection of abnormal pixels. It avoids the matrix inversion calculation that must be carried out in the conventional detection algorithms and the complexity is reduced. Compared with other detection algorithms on three different data sets, the experiment results show that the proposed algorithm presents obvious advantages in detection accuracy and excellent utility in practice.