Exploiting Spectral–Spatial Information Using Deep Random Forest for Hyperspectral Imagery Classification
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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 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".