Graph-Based Adaptive Network With Spatial-Spectral Features for Hyperspectral Unmixing
Hyperspectral unmixing aims to extract basic material (endmember) spectra and estimate their corresponding fractions (abundances) from observed pixels in hyperspectral images (HSIs). Recently, blind unmixing methods based on autoencoders (AEs) have gained significant attention due to their capabilit...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11003430/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Hyperspectral unmixing aims to extract basic material (endmember) spectra and estimate their corresponding fractions (abundances) from observed pixels in hyperspectral images (HSIs). Recently, blind unmixing methods based on autoencoders (AEs) have gained significant attention due to their capability to simultaneously obtain endmembers and abundances, as well as their strong performance. Also, many AE-based unmixing methods have explored the use of spatial information in HSIs to enhance unmixing performance. However, most of these methods fail to effectively integrate both local and global spatial-spectral features and lack adaptive selection and constraints for the extracted features. To address this issue, this article proposes an adaptive spatial-spectral unmixing method based on Graph. In the method, HSIs are treated as data on manifold structures, with superpixels serving as graph nodes to construct a global graph-structured data. We then use Graph Attention Network (GAT) to learn superpixel-level global spatial-spectral feature representations from this graph-structured data. Since the spatial-spectral feature learned by GAT is at the superpixel level, it is impossible to unmix each pixel using only this feature. Thus, we integrate a convolutional neural network to learn local discriminative spatial-spectral features. Attention mechanisms are further employed to select the features most relevant to unmixing. In addition, an Adaptive Abundance Constraint Module is proposed to dynamically enforce constraints on the extracted features. Experiments conducted on synthetic and real HSIs demonstrate the efficiency and superior performance of our proposed method compared to several existing unmixing approaches. |
|---|---|
| ISSN: | 1939-1404 2151-1535 |