A Lightweight Dual‐Branch Meta‐Learner for Few‐Shot HSI Classification With Cross‐Domain Adaptation
Bibliographic record
Abstract
ABSTRACT Hyperspectral imaging (HSI) plays a crucial role in urban area analysis from satellite data and supports the continuous advancement of intelligent cities. However, its practical deployment is hindered by two major challenges: the scarcity of reliable training annotations and the high spectral similarity among different land‐cover classes. To address these issues, this paper introduces a novel meta‐learning framework that synergistically combines knowledge transfer across domains with a dual‐adjustment mode (comprising intracorrection (IC) and interalignment (IA)), while ensuring end‐to‐end trainability. Our contributions are twofold. (1) We refine the 3D attention network TGAN into TGAN2 (3D ghost attention network v2) by replacing the original ghost blocks with ghost‐V2 modules and enlarging the receptive field to capture global context. (2) We propose a dual‐adjustment mode (comprising intracorrection (IC) and interalignment (IA)) to generate robust class prototypes and mitigate domain shift. These innovations are integrated into our overarching framework, DMCM2 (dual‐adjustment cross‐domain meta‐learning framework v2), which is unified by its end‐to‐end trainability and efficiency. The code and models will be publicly available at: https://github.com/YAO‐JQ/DMCM2 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".