Detecting an Optimal Scale Parameter in Object-Oriented Classification
Bibliographic record
Abstract
Avoiding spatial autocorrelation is the key to many research questions especially for field design, remote sensing data selection, and maximum spatial variation caption. Spatial variation across land cover types as well as the gradients inherent in ecotones can be captured in reflectance which is a spatially continuous variable. The spatial variation between reflectance values of any two pixels will depend on the lag distance beyond which pixels are no longer spatially autocorrelated. This paper demonstrates the utility of semivariogram for determining the lag distance in which pixels will be spatially autocorrelated. According to sampling theorem, objects should be sampled at half their width such that spatial resolution should be half of the semivariogram lag distance. As object-oriented classification is now the most broadly accepted classification method, scale parameter determination is the foremost important decision for determining the size of image objects. The scale parameter was adjusted during image segmentation to test how the size of image objects changed. The optimal scale parameter was chosen when the average distance between neighbouring image object centroids was near to the lag distance of the semivariogram. Results showed that the size of image objects reached a scaling threshold as the scale parameter was increased. When the scale parameter was adjusted to create image objects that exceeded this threshold, the segmentation was not able to accurately represent the spatial variation observed on the ground.
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 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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 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".