Analysing multitemporal SAR images for forest mapping
Bibliographic record
Abstract
The objective of this paper is twofold: first, to presents a generic approach for the analysis of Radarsat-1 multitemporal data and, second, to presents a multi classifier schema for the classification of multitemporal images. The general approach consists of preprocessing step and classification. In the preprocessing stage, the images are calibrated and registered and then temporally filtered. The resulted multitemporally filtered images are subsequently used as the input images in the classification step. The first step in a classifier design is to pick up the most informative features from a series of multitemporal SAR images. Most of the feature selection algorithms seek only one set of features that distinguish among all the classes simultaneously and hence a limited amount of classification accuracy. In this paper, a class-based feature selection (CBFS) was proposed. In this schema, instead of using feature selection for the whole classes, the features are selected for each class separately. The selection is based on the calculation of JM distance of each class from the rest of classes. Afterwards, a maximum likelihood classifier is trained on each of the selected feature subsets. Finally, the outputs of the classifiers are combined through a combination mechanism. Experiments are performed on a set of 34 Radarsat-1 images acquired from August 1996 to February 2007. A set of 9 classes in a forest area are used in this study. Classification results confirm the effectiveness of the proposed approach compared with the case of single feature selection. Moreover, the proposed process is generic and hence is applicable in different mapping purposes for which a multitemporal set of SAR images are available.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".