Transitive Semantic Alignment in Class-Conditioned Latent Diffusion for Martian Hyperspectral Mineral Mapping

Hyperspectral data possess rich but fragile geometric structure that is often obscured by noise, redundancy, and limited supervision. We propose \textit{Class-Conditioned Latent Diffusion Networks (CC-LDNs)}, which cast representation learning as a reverse-time diffusion process on a latent manifold. A lightweight 3D encoder maps hyperspectral patches to latent space, where classification constraints imposed on noisy latents regularize reverse diffusion dynamics. A label- and time-conditioned denoiser recovers clean representations via \textit{transitive semantic alignment}. Geometrically, CC-LDNs induce class-conditional basins of attraction that contract intra-class variability while preserving inter-class separation, yielding stable and well-conditioned latent structures. CC-LDN achieves OA of 96.51\%, 92.37\%, and 90.85\% on the HC, NF, and UP Martian datasets, respectively, outperforming eight state-of-the-art methods with fewer parameters than transformer-based models, validating latent diffusion as a geometry-grounded paradigm for hyperspectral learning under limited supervision.