A multiple constrained signal subspace projection for target detection in hyperspectral images

In the study, we develop a multiple constrained signal subspace projection (SSP) approach to target detection. Instead of using single constraint on target detection, we design an optimal filter with multiple constraints on desired targets by using SSP. The proposed SSP approach fully exploits the orthogonal property of two orthogonal subspaces: one denoted signal subspace containing desired and undesired/background targets; the other denoted noise subspace, which is orthogonal to signal subspace. By projecting the weights of the detection filter on the signal subspace, the proposed SSP can reduces some estimation errors in target signatures and alleviate the performance degradation caused by uncertainty of target signature. The SSP approach can detect desired targets, suppress undesired targets and minimize the interference effects. In experiments, we provide three methods in selecting multiple constraints of the desired target: Kmeans, principal eigenvectors and endmenber extracting techniques. Simulation results show that the proposed SSP with multiple constraints selected by K-means has better detection performance. Furthermore, the proposed SSP with multiple constraints is a robust detection approach which could overcome the uncertainty of desired target signature in real image data.