SAM-GFNet: Generalized Feature Fusion with Hierarchical Network for Hyperspectral Image Semantic Segmentation
Year: 2025
Type: article
Abstract: Visual foundation models exhibit significant potential for extracting generalized characteristics from imagery, which is crucial for remote sensing image interpretation tasks. However, current approaches underutilize these capabilities for hyperspectral image semantic segmentation. To bridge this gap, this study establishes SAM-derived generalized feature fusion with a hierarchical network for hyperspectral image semantic segmentation (SAM-GFNet), which integrates generalized features with hierarchical task-specific spatial–spectral characteristics for the segmentation task. Specifically, hyperspectral images are partitioned into multiple spectral groups, and SAM-induced generalized features are extracted within each group’s corresponding spectral range. To leverage multi-scale contextual information, the hierarchical hyperspectral image semantic segmentation architecture fuses multi-scale spectral–spatial features with SAM-induced generalized visual characteristics through a hierarchical fusion pattern. Furthermore, an efficient feature fusion module is further developed to adaptively integrate encoded features with decoder-generated characteristics, further enhancing semantic segmentation performance. The proposed semantic segmentation scheme is evaluated on WHU-OHS and WHU-H2SR benchmark datasets, achieving overall accuracies of 79.60% and 86.92% on two datasets, surpassing representative baseline methods by over 2.94% and 1.56%, respectively.
Source: Remote Sensing
DOI: 10.3390/rs17223662
Authors: Yinggang Guo, Xu Liu, Jinhong Rong, Fengmiao Shi, Chao Tang
Institution: Northwest Institute of Nuclear Technology
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4415990290