Spatial–Spectral ConvNeXt for Hyperspectral Image Classification
Bibliographic record
Abstract
Hyperspectral image (HSI) classification is a difficult task due to the heterogeneous spatial-spectral information, high-dimensiontality and noise effect in HSI. Lately, an enhanced convolutional approach, i.e., ConvNeXt, demonstrates stronger feature representation capability than the popular vision transformer (ViT) approaches. This paper presents a spatial-spectral ConvNeXt approach, called SS-ConvNeXt, for hyperspectral classification. To better learn the spatial and spectral information in HSI, the Spatial-ConvNeXt (Spa-cv) block, Spectral-ConvNeXt (Spe-cv) block and Spectral Projection Module (SPM) are respectively designed. The depthwise and pointwise convolutions are adopted to reduce the model size and prevent vanishing gradient. The proposed model is evaluated against 14 other state-of-the-art (SOTA) methods on 4 different HSI datasets. Moreover, extensive ablation studies are conducted to investigate the roles of building blocks in the proposed model. The results demonstrate that the proposed method not only can achieve high classification accuracy but also can better preserve class boundaries and reduce within-class noise. The codes of this work will be publicly available on Github.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".