DSFC-AE: A New Hyperspectral Unmixing Method Based on Deep Shared Fully Connected Autoencoder
Bibliographic record
Abstract
The pervasive presence of mixed pixels in hyperspectral remote sensing imagery poses a substantial constraint on the quantitative progress of remote sensing technology. Hyperspectral unmixing (HU) techniques serve as effective means to address this issue. In recent years, deep learning methods, particularly autoencoders (AEs), have been progressively employed in blind HU due to their compatibility with linear mixture models. However, most of the current advanced AE unmixing networks are based on a single-stage framework that conducts the unmixing task solely from a spectral perspective. This makes the rich spatial information ignored and makes it difficult for the network to obtain discriminative compression features while being susceptible to spectral variability and noise outliers. To address these issues, we propose a new deep shared fully connected autoencoder (DSFC-AE) unmixing network. The proposed DSFC-AE network comprises dual branches that utilize distinct data inputs for feature extraction: the original spectral data and coarse-scale spectral data obtained through superpixel segmentation. Furthermore, shared weight strategies are applied to the corresponding dimension reduction layers of the encoder, facilitating effective feature fusion. In addition, we integrate two constraint terms into the loss function, harnessing the sparsity of abundances and the geometric features of endmembers. We evaluate the DSFC-AE method against three traditional methods and four state-of-the-art deep learning algorithms using multiple real datasets. The results unequivocally demonstrate that the proposed network achieves significant improvements in both accuracy and stability.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".