SpectralTrain: A Universal Framework for Hyperspectral Image Classification
📝 Abstract
Hyperspectral image (HSI) classification typically involves large-scale data and computationally intensive training, which limits the practical deployment of deep learning models in real-world remote sensing tasks. This study introduces SpectralTrain, a universal, architecture-agnostic training framework that enhances learning efficiency by integrating curriculum learning (CL) with principal component analysis (PCA)-based spectral downsampling. By gradually introducing spectral complexity while preserving essential information, SpectralTrain enables efficient learning of spectral – spatial patterns at significantly reduced computational costs. The framework is independent of specific architectures, optimizers, or loss functions and is compatible with both classical and state-of-the-art (SOTA) models. Extensive experiments on three benchmark datasets – Indian Pines, Salinas-A, and the newly introduced CloudPatch-7 – demonstrate strong generalization across spatial scales, spectral characteristics, and application domains. The results indicate consistent reductions in training time by 2-7x speedups with small-to-moderate accuracy deltas depending on backbone. Its application to cloud classification further reveals potential in climate-related remote sensing, emphasizing training strategy optimization as an effective complement to architectural design in HSI models. Code is available at https://github.com/mh-zhou/SpectralTrain .
💡 Analysis
Hyperspectral image (HSI) classification typically involves large-scale data and computationally intensive training, which limits the practical deployment of deep learning models in real-world remote sensing tasks. This study introduces SpectralTrain, a universal, architecture-agnostic training framework that enhances learning efficiency by integrating curriculum learning (CL) with principal component analysis (PCA)-based spectral downsampling. By gradually introducing spectral complexity while preserving essential information, SpectralTrain enables efficient learning of spectral – spatial patterns at significantly reduced computational costs. The framework is independent of specific architectures, optimizers, or loss functions and is compatible with both classical and state-of-the-art (SOTA) models. Extensive experiments on three benchmark datasets – Indian Pines, Salinas-A, and the newly introduced CloudPatch-7 – demonstrate strong generalization across spatial scales, spectral characteristics, and application domains. The results indicate consistent reductions in training time by 2-7x speedups with small-to-moderate accuracy deltas depending on backbone. Its application to cloud classification further reveals potential in climate-related remote sensing, emphasizing training strategy optimization as an effective complement to architectural design in HSI models. Code is available at https://github.com/mh-zhou/SpectralTrain .
📄 Content
Hyperspectral imaging (HSI) provides densely sampled spectra per pixel over hundreds of contiguous bands, enabling fine-grained material discrimination across agriculture, Earth observation, and atmosphere-related applications [1][2][3]. Beyond generic high dimensionality, HSI exhibits domain-specific factors that make efficient training uniquely challenging for classification: (i) band-localized cues: class-discriminative information often concentrates in narrow, non-uniform wavelength intervals, so uniform downsampling or early heavy compression can remove task-relevant signals; (ii) spectral ordering and sensing topology: random or frequency-only truncation ignores the contiguous, instrument-induced structure of HSI cubes, causing spectral aliasing and unstable early gradients; (iii) cross-sensor spectral response and domain shift: fixed efficiency schedules tuned for one instrument may not transfer across sensors; and (iv) bandwidth-bound pipelines: compute and data movement scale with the number of bands, so starting from full spectra wastes early-epoch compute before coarse separations are learned [4,5]. These HSI-specific properties explain why efficiency recipes devised for RGB (e.g., frequency truncation, uniform band dropping) are especially insufficient when naively transplanted to HSI classification [6,7].
Motivated by EfficientTrain++ [8], we revisit efficiency from a training-schedule perspective rather than architectural changes. To the best of our knowledge, the HSI classification community has not systematically targeted training efficiency: most works still adopt standard training loops without explicitly staging learning to respect spectral structure and sensor heterogeneity. This gap motivates a curriculum that begins with information-preserving, low-cost spectra and progressively restores full spectral complexity as learning stabilizes.
We introduce SpectralTrain, a curriculum learning (CL) strategy tailored to the spectral dimension. Early phases replace the full spectrum with a principal component analysis (PCA)-compressed view that preserves dominant energy while suppressing noise; subsequent phases progressively increase the number of retained components until all bands are restored. This spectral curriculum stabilizes optimization, reduces Central Processing Unit (CPU)-Graphics Processing Unit (GPU) transfer and compute in initial epochs, and respects HSI band structure better than uniform or frequency-only truncation schemes. The procedure is agnostic to backbone, optimizer,
Curriculum learning (CL) organizes the training process by presenting easier elements before harder ones [18]. Representative variants integrate fuzzy-clustered curricula for semi-supervised classification [19] and design multi-task curricula for weakly supervised learning [20]. Self-paced and difficulty-aware schedules further use optimization feedback or uncertainty estimation to select and weight samples, improving robustness to label noise and class imbalance [21]. These methods typically operate along sample-, label-, or objective-level difficulty and have proven effective for convergence and generalization.
Beyond sample ordering, curricula can also control the input signal itself (e.g., image resolution or augmentation strength) to shape the optimization landscape. However, most such designs assume RGB image statistics. In hyperspectral imaging, information is distributed along a contiguous spectral axis with instrument-specific responses, and class-discriminative cues are often band-localized rather than uniformly spread. Directly borrowing RGB-oriented curricula can therefore discard informative bands or destabilize early optimization.
We position SpectralTrain as a curriculum grounded in the spectral axis: training starts from an information-preserving principal component analysis (PCA) compression of spectra and progressively restores bands as learning stabilizes. This spectral curriculum preserves task-relevant components while reducing early-epoch compute and I/O. It is orthogonal to spatial or semantic curricula and is compatible with a wide range of architectures and loss functions.
Hyperspectral imaging has demonstrated considerable potential in climate and environmental monitoring [22]. By capturing per-pixel reflectance across contiguous wavelengths, it supports aerosol and gas detection, vegetation stress analysis, and water quality assessment through material-specific spectral signatures. These capabilities provide a fine-grained basis for environmental state estimation beyond broadband sensors.
Despite this potential, adoption in meteorology and climate prediction remains comparatively limited. We study cloud-type classification as a practical entry point: the CloudPatch-7 dataset [23] assembles hyperspectral cloud patches to analyze spectral and spatial patterns relevant to early-stage weather recognition. Spectral cues associated with particle size, phase (ice versus liquid water)
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