Cross Spectral and Spatial Scale Non-local Attention-Based Unsupervised Pansharpening Network
Bibliographic record
Abstract
Pansharpening means fusing the low spatial resolution multispectral image (LRMSI) and the panchromatic (PAN) image to get the high resolution multispectral image (HRMSI). Due to the powerful feature learning ability of the deep-learning (DL), DL-based unsupervised fusion methods have been developed explosively. However, most of the fusion methods are difficult to fully explore and utilize the correct spatial and spectral correlation between the LRMSI, HRMSI, and PAN images. In addition, the CNN-dominated fusion framework is limited by its local feature learning without exploring the global feature dependency to further enhance the image feature. Therefore, to fully exploit the correct correlations between LRMSI, HRMSI, and PAN images and to explore the global feature dependency, we designed a cross-scale unsupervised fusion network (CSFNet). This network is composed of two cross spectral and spatial scale's nonlocal attention blocks to effectively fuse the LRMSI and PAN image features. And the fusion strategy is implemented by mapping the computed nonlocal similarity from the low resolution scale to the high resolution scale and outputs the reconstructed HRMSI feature. The experimental results on two datasets show that it achieves state-of-the-art performance compared to other fusion methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".