arXiv:2511.16084v1 Announce Type: new 
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.

SpectralTrain هو إطار عالمي جديد مصمم لتصنيف الصور الطيفية، يتناول تحديات البيانات واسعة النطاق والتدريب المكثف. من خلال دمج التعلم المنهجي مع تقليل الطيف القائم على تحليل المكونات الرئيسية، يعزز كفاءة التعلم مع تقليل التكاليف الحاسوبية. الإطار قابل للتكيف مع هياكل مختلفة وتم اختباره على مجموعات بيانات مرجعية تشمل Indian Pines وSalinas-A وCloudPatch-7.

SpectralTrain es un nuevo marco universal diseñado para la clasificación de imágenes hiperespectrales, que aborda los desafíos de los datos a gran escala y el entrenamiento intensivo. Al combinar el aprendizaje por currículos con un submuestreo espectral basado en el análisis de componentes principales (PCA), mejora la eficiencia del aprendizaje mientras reduce los costos computacionales. El marco es adaptable a diversas arquitecturas y ha sido probado en conjuntos de datos de referencia, incluidos Indian Pines, Salinas-A y CloudPatch-7.

SpectralTrain est un nouveau cadre universel conçu pour la classification d'images hyperspectrales, répondant aux défis des données à grande échelle et de l'entraînement intensif. En combinant l'apprentissage par curriculum avec un sous-échantillonnage spectral basé sur l'analyse en composantes principales (ACP), il améliore l'efficacité de l'apprentissage tout en réduisant les coûts computationnels. Le cadre est adaptable à diverses architectures et a été testé sur des ensembles de données de référence, notamment Indian Pines, Salinas-A et CloudPatch-7.

SpectralTrain is a new universal framework designed for hyperspectral image classification, addressing the challenges of large-scale data and intensive training. By combining curriculum learning with PCA-based spectral downsampling, it enhances learning efficiency while reducing computational costs. The framework is adaptable to various architectures and has been tested on benchmark datasets including Indian Pines, Salinas-A, and CloudPatch-7.

SpectralTrain: A Universal Framework for Hyperspectral Image Classification

arXiv:2405.09676v2 Announce Type: replace-cross 
Abstract: Classical results in asymptotic statistics show that the Fisher information matrix controls the difficulty of estimating a statistical model from observed data. In this work, we introduce a companion measure of robustness of an estimation problem: the radius of statistical efficiency (RSE) is the size of the smallest perturbation to the problem data that renders the Fisher information matrix singular. We compute RSE up to numerical constants for a variety of testbed problems, including principal component analysis, generalized linear models, phase retrieval, bilinear sensing, and matrix completion. Interestingly, we observe a precise reciprocal relationship between RSE and the intrinsic complexity/sensitivity of the problem instance, paralleling the classical Eckart-Young theorem in numerical analysis. To establish our results, we develop theory for spectral functions of measures that extends well-known results from matrix analysis and eigenvalue optimization$-$a contribution that may be of interest beyond our immediate findings.

دراسة حديثة تقدم مفهوم نصف قطر الكفاءة الإحصائية (RSE)، وهو مقياس جديد يقيس متانة مشاكل التقدير من خلال تحديد أصغر اضطراب يجعل مصفوفة معلومات فيشر مفردة. تشمل هذه الأبحاث مجموعة متنوعة من النماذج الإحصائية، بما في ذلك تحليل المكونات الرئيسية والنماذج الخطية العامة، مما يبرز العلاقة بين RSE وتعقيد هذه النماذج.

Un estudio reciente introduce el radio de eficiencia estadística (RSE), una nueva medida que cuantifica la robustez de los problemas de estimación al determinar la perturbación más pequeña que hace que la matriz de información de Fisher sea singular. Esta investigación abarca varios modelos estadísticos, incluyendo el análisis de componentes principales y modelos lineales generalizados, destacando la relación entre el RSE y la complejidad de estos modelos.

Une étude récente introduit le rayon d'efficacité statistique (RSE), une nouvelle mesure qui quantifie la robustesse des problèmes d'estimation en déterminant la plus petite perturbation qui rend la matrice d'information de Fisher singulière. Cette recherche couvre divers modèles statistiques, y compris l'analyse en composantes principales et les modèles linéaires généralisés, mettant en lumière l'interaction entre le RSE et la complexité de ces modèles.

A recent study introduces the radius of statistical efficiency (RSE), a new measure that quantifies the robustness of estimation problems by determining the smallest perturbation that makes the Fisher information matrix singular. This research spans various statistical models, including principal component analysis and generalized linear models, highlighting the interplay between RSE and the complexity of these models.

The radius of statistical efficiency

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SpectralTrain: A Universal Framework for Hyperspectral Image Classification

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