Unsupervised classification of agricultural land cover using polarimetric synthetic aperture radar via a sparse texture dictionary model
Bibliographic record
Abstract
A sparse texture dictionary learning method for unsupervised land cover classification is presented. The method takes the stance that land cover in remote sensing data is best analysed in texture patches rather than localized pixels. To this end, a feature vector is designed that describes local texture information in a spatially coherent manner. This texture model is extracted for each pixel in the scene. A sparse dictionary of global texture models is then learned to characterize the underlying texture distribution of the scene in a simplified manner. An unsupervised classifier is learned using these global texture models for grouping pixels exhibiting high similarity. Being an unsupervised classifier, the class labels that are learned are unbiased toward human interpretation of the scene, and rather are learned according to the texture information. The method is validated using polarimetric SAR data over a Flevoland, Netherlands agriculture scene, but may be generalized to any remote sensing data. Promising experimental results show how the proposed method retains the spatial coherence of crops, and attains higher accuracy than recent unsupervised and supervised classification methods using the same data.
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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 | 0.000 |
| 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) | 0.000 | 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".