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Abstract

Hyperspectral image classification (HSIC) relies on static spatial-spectral representations learned from labeled samples, limiting its ability to model structured dependencies in the latent representation space. To address this limitation, this work proposes Stochastic Latent World Modeling (LWM), a trajectory-based representation-learning framework that enriches classification through stochastic latent-state prediction. Given a hyperspectral patch, LWM tokenizes and encodes spectral and spatial information separately using dedicated transformer branches, then fuses their outputs into a unified representation. This representation is further organized into an environment state that captures center, neighborhood, spectral, and global contextual information. A stochastic latent transition module then predicts multiple future latent states through Gaussian parameterization and reparameterization, with recurrent refinement used to model dependencies across successive states. The resulting latent trajectory is adaptively aggregated by a rollout planner to construct a planned representation, which is integrated with the base representation for final classification. By extending static spatial-spectral encoding with stochastic multi-step latent-state modeling, LWM enables the classifier to exploit structured dependencies in the representation space and provides richer features for HSIC. Across five benchmark datasets, LWM achieves overall accuracies ranging from 89.21% to 99.52%, demonstrating consistent classification performance under limited-label settings.

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