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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".