Exploiting Spectral–Spatial Information Using Deep Random Forest for Hyperspectral Imagery Classification
Why this work is in the frame
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Bibliographic record
Abstract
In recent years, deep learning methods have been widely applied to hyperspectral image (HSI) classification. Besides convolutional neural network (CNN)-based deep learning, deep random forest (RF)-based method, such as densely connected deep RF (DCDRF), was also developed for HSI classification which utilized the spectral–spatial information to improve the classification accuracy. In DCDRF, evenly distributed image patches with a fixed patch size are utilized to extract the spatial information of ground objects. However, the spatial information in each patch is not always correct, especially when the patch center is close to the edge of ground objects. In this letter, we propose a new classification method called spectral–spatial deep RF (SSDRF) which can fully utilize the spatial information existing in HSIs to further improve the classification accuracy. The joint region that combines both the fixed-size patch and shape-adaptive superpixel is proposed to exploit more accurate spatial information. The RF used in the classification model is replaced by extremely random forest (EF) to avoid overfitting. Moreover, the majority voting is conducted within superpixels and among different scales of superpixels to optimize the classification. The experimental results on three HSIs demonstrate that the proposed SSDRF can achieve satisfactory classification results and outperforms patched-based DCDRF.
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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 | 0.001 |
| 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