From Pixels to Geographic Objects in Remote Sensing Image Analysis
Bibliographic record
Abstract
Traditional image analysis methods are mostly pixel-based and use the spectral differences of landscape elements at the Earth surface to classify these elements or to extract element properties from the Earth Observation image. Geographic object-based image analysis (GEOBIA) has received considerable attention over the past 15 years for analyzing and interpreting remote sensing imagery. In contrast to traditional image analysis, GEOBIA works more like the human eye–brain combination does. The latter uses the object’s color (spectral information), size, texture, shape and occurrence to other image objects to interpret and analyze what we see. GEOBIA starts by segmenting the image grouping together pixels into objects and next uses a wide range of object properties to classify the objects or to extract object’s properties from the image. Significant advances and improvements in image analysis and interpretation are made thanks to GEOBIA. In June 2010 the third conference on GEOBIA took place at the Ghent University after successful previous meetings in Calgary (2008) and Salzburg (2006). This special issue presents a selection of the 2010 conference papers that are worked out as full research papers for JAG. The papers cover GEOBIA applications as well as innovative methods and techniques. The topics range from vegetation mapping, forest parameter estimation, tree crown identification, urban mapping, land cover change, feature selection methods and the effects of image compression on segmentation. From the original 94 conference papers, 26 full research manuscripts were submitted; nine papers were selected and are presented in this special issue. Selection was done on the basis of quality and topic of the studies. The next GEOBIA conference will take place in Rio de Janeiro from 7 to 9 May 2012 where we hope to welcome even more scientists working in the field of GEOBIA.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".