Orthogonal-Heading Wavelength-Resolution SAR Image Stack Fusion-Based Foliage-Penetrating Vehicle Detection

This paper presents an orthogonal-heading wavelength-resolution SAR (WRSAR) target detection framework that fuses multi-heading image stacks for foliage-penetrating (FOPEN) vehicle detection. First, a low-rank–sparse decomposition is applied to very-high-frequency (VHF), ultra-wideband (UWB) WRSAR stacks to suppress vegetation clutter and enhance target contrast. The clutter-suppressed sparse stacks acquired from orthogonal headings are then fused to enrich target scattering characteristics. Finally, a Rayleigh-entropy statistic computed on the fused sparse stack is used to represent discontinuous positional changes. Based on the non-negative nature of WRSAR amplitudes for both clutter and FOPEN targets, we introduce a non-negative constrained tensor robust principal component analysis (NCTRPCA) to improve sparsity in the stack components. Furthermore, since Shannon differential entropy has no tunable parameter, we replace Shannon entropy with RE in this work and derive its closed-form expression for the proposed detector. Experiments on the publicly available multi-heading, multi-temporal CARABAS II dataset show that the proposed orthogonal-heading WRSAR fusion achieves higher FOPEN vehicle detection performance than recent state-of-the-art methods while maintaining moderate computational cost.