Supervised Region-Based Segmentation of Quickbird Multispectral Imagery
Bibliographic record
Abstract
The segmentation of very high resolution (VHR) satellite imagery (such as Digital Globe QuickBird) is becoming increasingly important to geo-related applications. New sensors provide the ability to discriminate large scale objects that were not discernable with lower resolution satellite imagery such as Landsat TM. VHR satellite images also exhibit an incredible dynamic grey-value variety. These features, among others, impede existing algorithms developed for lower resolution satellite imagery to operate within the same degree of accuracy. This paper proposes a supervised approach to the segmentation of QuickBird multispectral imagery through the integration of the Hierarchical Split Merge Refinement (HSMR) framework. The HSMR framework was originally developed by Ojala and Pietikainen [1999] for unsupervised segmentation of textured areas. In this approach, user identified regions are employed to guide HSMR algorithmic processes. User knowledge is brought to segmentation and it is hypothesized that this will improve stabilization in HSMR segmentation across a variety of QuickBird 2.44 m multispectral satellite image scenes and improve control of segmentation at different scales.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".