A kernel-based signature space orthogonal projection（KSSP） technique is proposed for nonlinear subpixel target detection in hyperspectral imagery.As a nonlinear version of the signature space orthogonal projection（SSP）

the SSP is adopted in a high-dimension feature space after the pixels of input space are mapped into the feature space via nonlinear mapping.The kernel trick allows the KSSP ignor the actual nonlinear mapping.Experimental results of simulated and real data prove that the proposed KSSP approach outperforms the SSP method in target detection

and improves the robustness to noise.