Depthwise Separable ResNet in the MAP Framework for Hyperspectral Image Classification
Why this work is in the frame
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Bibliographic record
Abstract
To build small and efficient neural networks for hyperspectral image (HSI) classification, this letter presents a depthwise separable residual neural network (ResNet). This approach, motivated by the popular MobileNet architecture, decomposes the traditional spatial-spectral convolution operation into a spatial-independent pointwise spectral convolution and a spectral-independent depthwise spatial convolution. It allows the separation of spectral and spatial information in HSI and also greatly reduces the network size to prevent the overfitting issue. To better preserve the class boundaries and edges, the proposed ResNet is integrated into a maximum a posteriori (MAP) framework to allow the use of the conditional random field (CRF) model. The experiment results on benchmark HSI scene demonstrate that the proposed ResNet compares favorably with several popular deep learning HSI classifiers and that the ResNet-CRF approach achieves higher accuracy and better boundaries among neighboring classes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it