Hyperspectral Image Classification With Stacking Spectral Patches and Convolutional Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Jointly combining the spatial and spectral features has proved to dramatically improve the performance of classifying hyperspectral image. Recently, utilizing neural networks to automatically model the spatial-spectral feature representations for hyperspectral images has become of great interest. This paper proposes a simple but innovative framework to classify hyperspectral image with two shallow convolutional neural networks (CNNs). First, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">principal component analysis whitening</i> is applied to decorrelate hundreds of spectral bands. Instead of selecting the principal components to reduce the spectral dimensionality, we retain all the spectral bands but compress the image cuboid into a one-channel spectral quilt by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stacking spectral patches</i> . In this way, not only all the spectral information is retained but also the computational complexity of training a neural network is reduced compared with conventional networks that directly input the spectral volumes. Moreover, the spectral quilt will contain some novel textural patterns that are effective at distinguishing classes. Two shallow CNNs are then applied to classify the spectral quilts. As shown in the experiments, both networks can outperform the standard analysis methods.
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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 | 0.001 |
| 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