Object-oriented and pixel-based image classification using Landsat multispectral and Hyperion hyperspectral imagery in boreal conditions / by Jevon S. Hagens.
Bibliographic record
Abstract
"Current environmental trends dictate a need for new methods, initiatives, and technologies that provide reliable, up-to-date forest information. Canada, which is home to ten percent of the Earth's forests, has made national and international commitments to better monitor the sustainable development of its forest ecosystems. In Ontario, the Ministry of Natural Resources monitors its natural resources through the Ontario Land Cover Database (OLCD). The OLCD is a large area land classification that uses Landsat multispectral imagery with a traditional pixel-based classifier. The goal of this thesis is to explore new ways to improve upon large area land classifications such as the OLCD. This thesis evaluates two alternative approaches: (1) it compares Landsat-5 TM multispectral imagery to Hyperion hyperspectral imagery, and (2) it compares a traditional pixel-based classifier to eCognition's object-oriented image classifier. Eight boreal cover classes were used consisting of water, wetland (aggregated marsh, fen and bog), black spruce, jack pine, mixedwood, dense deciduous, sparse deciduous and clearcuts.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".