QUANTIFYING IMPACTS OF SPATIAL RESOLUTION ON PIXEL AND OBJECT-BASED METHODS OF IMAGE CLASSIFICATION: A CASE STUDY OF IDENTIFYING ESTUARINE MORPHOLOGY IN COBEQUID BAY, NOVA SCOTIA, CANADA
Bibliographic record
Abstract
Image processing methods can be used to classify land cover and phenomena fromsatellite imagery according to their spectral characteristics and identify target features. These methodscanprovide a relatively efficient approach to processing many images and to measuringchange of features over time. Selecting an appropriate data source and classification method, however, requires considerations such as the scale of the process under investigation, spectral differences between target areas and their surroundings, and technical limitations for the analyst.The Salmon River estuary within Cobequid Bay, Nova Scotiawas chosen to evaluate the impact of spatial resolution on the use of satellite imagery to identify tidal bars. Images were acquired from four satellite systems (PlanetScope, RapidEye, Sentinel-2, Landsat-5) representing arange of spatial resolutions(3m to 30m). Both traditional pixel-based methods (i.e., supervised, unsupervised classification), and object-based image classification methods were used to identify sediment bars within the estuary, and then assessed for classification accuracy.All image types could be classified to at least 80% overall accuracywith a Cohen’s Kappa coefficient of 0.9using at least one method. The research identifiedtrends related to classification result and increasing spatial resolution including: 1) decreasing reliability of unsupervised classification;2) increased single-pixel errors in supervised classifications, despite overall product reliability;and 3) formation of increasingly meaningful pixel groupings for object-based analysis. Differences in appropriate classification method are considered to be a result of the relationship between scale of phenomenon being mapped and the spatial resolution at which it is represented; when increasing spatial resolution, there is a shift away from presence of mixed-pixels towards the dominance of multi-pixel objects, the classification of which is better suited to object-based as opposed to pixel-based methods. The results suggesta Modifiable Areal Unit Problem-based framework to consider large pixels and object created from small pixels each as viable areal units of analysis for processes at this scale. Keywords: Keywords:Remote sensing, scale issue, estuary, geomorphology, Bay of Fundy Pages: 66 Supervisor: Christopher Greene
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".