Hyperspectral band selection seeks to identify a compact subset of informative spectral channels that preserves task–relevant information while mitigating the storage, transmission, and computational burdens imposed by high–dimensional data. Yet prevailing techniques face two pervasive limitations: (i) scoring- or ranking-based methods assess bands independently, overlooking the joint dependency that determine their true utility; and (ii) combinatorial search approaches, though theoretically exhaustive, require prohibitive enumeration that is incompatible with the scale and end-to-end nature of modern deep-learning pipelines. We recast band selection as a combinatorial inference problem and propose a task-agnostic framework that embeds a learnable Band Selection Layer equipped with an Expectation–Maximization–driven Sparsity Loss The E-step efficiently enumerates the expected likelihood of allk-out-of-Bband subsets via dynamic programming, thereby making implicit dependencies explicit; the M-step optimises band importances toward a provablyk-sparse solution without post-hoc thresholding. Comprehensive theoretical analysis proves the absence of spurious local maxima and guarantees convergence to an exact sparse optimum. Extensive experiments on three public benchmarks (KSC, HT2013, HT2018), two auxiliary tasks (anomaly and target detection), and six classifiers demonstrate that the proposed method consistently surpasses state-of-the-art baselines. The results confirm that EM-guided sparsification not only stabilises the sparsity pattern but also yields interpretable inter-band dependency structures, making the framework a robust and broadly applicable tool for hyperspectral analysis and other sparsity-oriented vision problems.

Image understanding and analysis primarily rely on object appearances. However, when faced with challenges such as occlusion, camouflage, and small targets in surveillance scenarios, the discriminative features of objects cannot be effectively extracted. This paper proposes the utilization of hyperspectral imaging techniques as a solution to these issues, aiming to unlock new potential in the field. Hyperspectral images have the unique capability to identify a variety of materials, thereby offering a distinct advantage in surveillance. However, existing hyperspectral image datasets are not specifically tailored for image classification tasks within surveillance scenarios. To address this issue, we introduce an innovative hyperspectral image dataset designed explicitly for real-world surveillance, with the goal of setting a new benchmark for material classification. Our aspiration extends beyond merely deploying deep learning methods for hyperspectral material classification, aiming to contribute insightful understanding of spectral patterns inherent in natural surveillance scenes. The proposed dataset is currently the largest of its kind and the first one designed specifically for surveillance scenarios. It encompasses 128 spectral bands and provides annotations for 28 common material categories. Furthermore, we introduce a novel texture-metric-based spatial and spectral fusion network, meticulously crafted to accommodate our unique scenario and dataset. This model significantly outperforms existing networks in enhancing the fusion of spatial and spectral features, achieving state-of-the-art results on both our proposed dataset and existing public hyperspectral image classification datasets. For access to this unique dataset, please visithttps://github.com/robberban/HIC-for-Surveillance.git.