Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Based on a Gaussian Mixture Model and Error Ellipse
Bibliographic record
Abstract
Relative radiometric normalization is often required in time series analysis of satellite Earth observations such as land cover change detection. Normalization process reduces the radiometric differences caused by changes in the environmental conditions during the acquisition of multitemporal satellite images. In this paper, we proposed an efficient and automatic method based on Gaussian mixture model (GMM) to find a set of subjectively chosen invariant pixels. A linear model, based on Error Ellipse, was then adjusted to normalize the subject image. The proposed method involves two main steps; in the first step, invariant pixels, which are known as most probable unchanged pixels, were obtained by analyzing image differences estimated by GMMs. Then, these pixels were used to model the relationship between two multitemporal images. To evaluate the proposed method in real analysis scenarios, three multitemporal datasets acquired by different satellite sensors such as Ikonos, Quickbird, SuperView-1, and Worldview-2 were analyzed. These images were collected before and after the 2011's Japan and the 2004's Indonesia Tsunamis, and the 2017's Iran–Iraq earthquake. Experimental results demonstrated that the proposed method can considerably improve the radiometric variations between temporal images for change detection applications.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".