SPDB-ECEM: A Spectral–Polarimetric Image-Based Method for Feature Target Detection

Target detection in complex backgrounds remains challenging because targets and backgrounds are often insufficiently separable in a single spectral or polarimetric feature space. In addition, conventional detectors usually have limited capability to exploit multidimensional heterogeneous features, which may result in false alarms and missed detections. To address these issues, this paper proposes a target detection method based on the collaborative enhancement of spectral–polarimetric heterogeneous information. First, density peak clustering (DPC) is employed to select representative and discriminative bands from the original multispectral data, thereby reducing spectral redundancy and concentrating target-related information. Second, a Spectral–Polarimetric Dual-Branch Ensemble-Based Cascaded Constrained Energy Minimization detector, termed SPDB-ECEM is developed. In each cascaded layer, spectral and polarimetric branches are constructed separately to model target responses in different feature spaces, while cross-branch response guidance is introduced to promote the intra-layer interaction and collaborative enhancement of heterogeneous information. Meanwhile, a reliability-gated fusion strategy is designed to adaptively balance the spectral and polarimetric responses according to the polarimetric response and the degree of linear polarization (DoLP). To further suppress local false alarms, background suppression, local consistency enhancement, and orthogonal-complement constraints are incorporated into the cascaded detection process to improve target-response consistency and reduce background interference. Finally, threshold-constrained OTSU (t-OTSU) is adopted to segment the fused response map and generate the target mask. The proposed framework establishes an integrated processing pipeline of band selection, dual-branch enhancement, and threshold segmentation, enabling effective complementarity between spectral and polarimetric information. Experimental results demonstrate that the proposed method improves target saliency and boundary separability in complex backgrounds, enhances the robustness of target enhancement and detection, and provides an effective solution for spectral–polarimetric joint target detection.