Spectral–Spatial and Cascaded Multilayer Random Forests for Tree Species Classification in Airborne Hyperspectral Images
Bibliographic record
Abstract
The rapid development of remote sensing sensors has made it possible to collect airborne hyperspectral data with high spectral and spatial resolution. Such data can provide valuable information to identify tree species in the forest. However, it is a challenge to efficiently utilize the abundant spectral information and complex spatial information within the data. In this article, a Spectral-Spatial and Cascaded Multilayer Random Forests (SSCMRF) method is proposed to classify tree species in the high spatial resolution hyperspectral image. The SSCMRF adopts two classification stages to fully exploit the spatial information within shape-adaptive superpixels and shape-fixed patches. Two different kinds of spatial information are integrated by concatenating the output of the superpixel-based classification and the spectral features as the input of the patch-based classification. To demonstrate the superiority of the proposed SSCMRF, experiments are conducted with an airborne hyperspectral data set of a forest area with the spatial resolution of 1 m. Training with 2.5% randomly selected ground truth samples, the proposed SSCMRF achieves a classification accuracy of 97.50% within 6 minutes. In addition, the experiment results demonstrate that the proposed SSCMRF outperforms some state-of-art spectral-spatial classification models in terms of quantitative metrics and visual quality on the classification map.
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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 | 0.001 | 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 | 0.000 |
| 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".