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Subpixel Spectral Variability Network for Hyperspectral Image Classification
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AI
DOI：10.1109/TGRS.2025.3535749.png)
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Abstract
En 中文
Deep learning-based frameworks have shown great potential in the field of hyperspectral image (HSI) classification owing to their superior modeling capabilities. However, the existence of mixed pixels and spectral heterogeneity limits the discriminant performance of the classifier, which makes it impossible to distinguish the mixed spectra effectively in actual scenarios. To address this gap, we propose a subpixel spectral variability network (S(2)VNet) for hyperspectral image classification, which incorporates complete subpixel information and class features modeled by spectral variability and nonlinear mixture characteristics to enhance classification performance. S(2)VNet is capable of extracting endmembers and abundances based on the nonlinear autoencoder (AE) framework and estimating variability parameters by simultaneously considering scaling factors and perturbation terms to ensure accurate endmember construction. The enhanced subpixel fusion module is further designed to automatically integrate three aspects of abundances, spectral cosine correlation information and pixel-level class features to provide a robust joint representation for the classifier. Extensive experiments on four public HSI datasets demonstrate the superiority and generalization of the proposed method when benchmarked with the state-of-the-art methods.
Keywords:
Feature extraction
Transformers
Hyperspectral imaging
Data mining
Perturbation methods
Computer architecture
Autoencoders
Training
Three-dimensional displays
Representation learning
Image classification
nonlinear autoencoder (AE) network
remote sensing
spectral variability
subpixel information
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W