Forest Classification Using Simulated Compact Polarimetry Data and Deep Learning Networks
Bibliographic record
Abstract
In the last two decades, among various Synthetic Aperture RADAR (SAR) imaging modes, Compact Polarimetry (CP) mode has drawn a lot of attention due to less complex imaging system, mass and data rate reduction, and also greater swath width.Having such advantages makes this data very useful for large-scale target mapping, such as forest classification.Different methods have been proposed for forest classification using CP mode, all of which are based on feature extraction.The accuracy of these methods depends on the discrimination of the extracted features.Among these methods, deep learning networks have almost automated the feature extraction phase and obtained impressive results, especially in the classification task.In this paper, the ability of deep learning networks is investigated by using CP mode data in forest classification.The study area of this paper is Petawawa forest located in Ontario, Canada, and the data being used are simulated CP data, Full Polarimetric (FP) data, and also reconstructed Pseudo Quad (PQ) data acquired from RADARSAT-2 in C-band.The proper deep learning network for automatic feature extraction is designed and the classification is performed on CP, FP, and PQ data.The results from all mode classifications are evaluated and compared with each other and also with the results from Wishart classifier and Support Vector Machine (SVM).The results of this paper show that using deep learning networks improves the classification accuracy of CP and PQ modes.
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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".