Multispectral Pansharpening Based on Multisequence Convolutional Recurrent Neural Network
Bibliographic record
Abstract
Multispectral (MS) pansharpening is defined as the fusion of spatial information in panchromatic (PAN) image and spectral information in MS image. In this work, we propose a MS pansharpening based on multi-sequence convolutional recurrent neural network (MCRNN). The proposed MCRNN contains two sub-networks (shallow feature extraction sub-network and deep feature fusion sub-network). In the shallow feature extraction sub-network, PAN and MS images are superimposed in the spectral dimension as multi-sequence data. A convolutional neural network (CNN) based on residual learning is then used to obtain the feature maps from multi-sequence data. In the deep feature fusion sub-network, since MS and PAN images are highly correlated, a convolutional recurrent neural network (ConvGRU) belonging to RNN is used to model adjacent and across-band relationships between these feature maps to capture the local and global correlations of the features in different bands. The global average pooling is then performed on the output results to yield the pansharpening result. Several datasets are tested at reduced and full resolution experiments, the experimental results show that the performance of the proposed MCRNN is superior to the traditional pansharpening methods. The source code is available athttps://github.com/HZC-1998/Multi-Sequence-Convolutional-Recurrent-Network-for-Pansharpening.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".