Transfer learning RGB models to hyperspectral images with trainable tensor decompositions

2026-05-27

AI Summary

This work addresses the challenge of directly transferring pretrained RGB vision models to hyperspectral image analysis, where a fundamental mismatch exists between the three-channel input assumption and the high-dimensional spectral nature of hyperspectral data. To overcome this, the authors propose a novel partially trainable tensor decomposition strategy that decouples pretrained convolutional kernels into spatial and spectral components. The original three-channel spectral part is replaced with a high-dimensional, learnable spectral component, thereby constructing new filters tailored for hyperspectral inputs. This approach uniquely integrates trainable tensor decomposition into transfer learning, preserving the powerful spatial feature extraction capabilities of the original model while effectively modeling hyperspectral characteristics. Extensive experiments demonstrate that the proposed method significantly outperforms existing transfer learning approaches across multiple hyperspectral datasets, achieving both higher accuracy and improved robustness.

Abstract

Transfer learning makes it possible to use large vision networks on a variety of domains, by specializing their models' general filters to new tasks. However, these networks assume the input images to have 3 input channels, making them incompatible with multi- or hyperspectral images. Current approaches that mitigate this incompatibility sacrifice information in either the image, or the model. This work proposes a novel approach that preserves the image and spatial information present in the model by using partially trainable tensor decompositions. We create such decompositions of pretrained convolutional filters, separating the filters into spatial and spectral components. The spectral components are then replaced with trainable components of higher channel dimensionality. This creates hyperspectral filters that can specialize to new datasets, while retaining the spatial patterns of the original filter. Experiments on a variety of hyperspectral datasets show that our approach is more accurate and robust than other hyperspectral transfer learning methods.

Problem

• transfer learning
• hyperspectral images
• tensor decompositions
• pretrained models
• multi-spectral imaging

Innovation

• transfer learning
• hyperspectral imaging
• tensor decomposition
• convolutional neural networks
• spectral-spatial decomposition