Full fuzzy land cover mapping using remote sensing data based on fuzzy<i>c</i>-means and density estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AbstractThe three stages in supervised digital classification of remote sensing data are training, classification, and testing. The commonly adopted approaches assume that boundaries between classes are crisp and hard classification is applied. In the real world, however, as spatial resolution decreases significantly, the proportion of mixed pixels increases. This leads to vagueness or fuzziness in the data, and in such situations researchers have applied the fuzzy approach at the classification stage. Some researchers have tried fuzzy approaches at the training, classification, and testing stages (full fuzzy concept) using statistical and artificial neural network methods. In this paper a full fuzzy concept has been presented, at a subpixel level, using density estimation using support vector machine (D-SVM) and fuzzy c-means (FCM) approaches. These approaches (SVM and FCM) were evaluated with respect to a fuzzy weighted matrix. In this test study using a four-channel dataset, a comparison of methods has found that a D-SVM function using a Euclidean norm yields the best accuracy.Les trois étapes de la classification numérique dirigée des données de télédétection sont l'entraînement, la classification et la validation. Les approches adoptées généralement supposent que les frontières entre les classes sont nettes et on applique ainsi des classifications dures. Toutefois, dans la réalité, lorsque la résolution spatiale diminue significativement, la proportion de pixels mixtes augmente. Ceci entraîne une imprécision ou un flou dans les données et, dans de tels cas, les chercheurs ont appliqué une approche floue au stade de la classification. Certains chercheurs ont essayé des approches floues aux stades de l'entraînement, de la classification et de la validation (concept flou complet) utilisant des méthodes statistiques et des réseaux de neurones artificiels. Dans cet article, un concept flou complet est présenté, au niveau du sous-pixel, basé sur l'utilisation des approches D-SVM de même que FCM. Ces approches (SVM et FCM) ont été évaluées par rapport à la matrice floue pondérée. Dans cette étude test, basée sur l'utilisation d'un ensemble de données de quatre bandes, une comparaison des méthodes a montré qu'une fonction D-SVM utilisant une norme euclidienne donne la meilleure précision.[Traduit par la Rédaction]
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it