SpectralTrain: A Universal Framework for Hyperspectral Image Classification

2025-11-20

AI Summary

To address the challenges of large-scale data and high computational cost in hyperspectral image (HSI) classification, this paper proposes SpectralTrain, a general-purpose training framework. SpectralTrain innovatively integrates curriculum learning with principal component analysis (PCA)-driven spectral band sampling, progressively introducing spectral complexity to achieve efficient, architecture-agnostic training acceleration—without modifying model architectures or optimizers. It is compatible with diverse deep learning models. Experiments on Indian Pines, Salinas-A, and CloudPatch-7 demonstrate 2–7× speedup in training time, with controlled accuracy degradation (average drop <1.5%). The framework exhibits strong generalization across varying spatial resolutions, spectral characteristics, and remote sensing scenarios. Its core contribution is the establishment of the first network-design-agnostic, curriculum-based spectral training paradigm, decoupling spectral learning strategy from specific model implementations.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Reduces computational costs in hyperspectral image classification training
• Enables efficient spectral-spatial pattern learning through curriculum learning
• Provides architecture-agnostic framework compatible with various deep learning models

Innovation

• Integrates curriculum learning with PCA-based spectral downsampling
• Gradually introduces spectral complexity while preserving essential information
• Achieves 2-7x training speedups with architecture-agnostic framework