Segmentação de imagens de alta resolução utilizando o programa SMAGIC
Bibliographic record
Abstract
Segmentation and classification of high resolution image is not an easy task. High intra-class variance acts as noise and directly affects classification results. A range of algorithms has been developed in the past decades to extract objects-like shapes from the image. Many of these are not able to work properly with noisy data like radar images or even high resolution images of the Earth. In this paper a new software is tested to process 1-meter Ikonos images: SMAGIC. Originally created as a tool to identify different types of ice from Radarsat images at Canada, SMAGIC can now process multivariate data. The algorithm is unique in its approach. The approach is a hybrid one that makes use of a watershed segmentation and a Markov Random Fields paradigm. In this study a set of three Ikonos images samples was segmented and by SMAGIC. Field work was done to recognize the area and validation data for used in the classification process and to label the classes obtained with SMAGIC. In addition, same images were classified by the ECHO algorithm used as a benchmark comparison. All results were interpreted visually. SMAGIC was able to produce good classification results that generally outperformed traditional classification methods without the necessity to use training data. SMAGICs algorithm is described as well as the general testing design. Some insight is given about future developments of SMAGIC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".