Textural processing of multi-polarization SAR for agricultural crop classification
Bibliographic record
Abstract
Three techniques for generating texture statistics are examined: the gray-level co-occurrence matrix (GLCM), the gray-level difference vector (GLDV) and the neighboring gray-level dependence matrix (NGLDM). The objective of these statistical approaches is to translate visual texture properties into quantitative descriptors in a manner that they can be used to discriminate relevant land features using additional image processing techniques. These second-order statistical methods are used to generate texture features from C-HH and C-HV airborne synthetic aperture radar (SAR) data collected on July 10, 1990 over an agricultural area in southern Ontario Canada. Texture features generated from the GLCM, GLDV and NGLDM are classified individually using a k-nearest neighbor (k-NN) supervised classifier. The greatest classification improvement (/spl ap/20%) was observed with the mean and correlation texture features derived from the GLCM. However, the selection of a specific second-order statistical technique may not be critical, since similar classification improvements were observed for the GLCM, GLDV and NGLDM statistical techniques. The results reported here highlight the importance of texture processing to methods of classifying agricultural crops using SAR data.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".