Change Detection Analysis using Information Theoretic Measures on SAR Images
Bibliographic record
Abstract
We discuss the use of two statistics for change detection in the context of Synthetic Aperture Radar (SAR) imagery. We show their application to a bi-temporal pair of HH and VV channel intensity images from RADARSAT-2 of an agricultural scene in Winnipeg, Manitoba, Canada. The images were acquired on 7th and 31st July 2012. One of the statistics is based on a stochastic distance viz., Hellinger distance, while the other is based on the Shannon entropy which provides a measure of randomness. We have assumed the Gamma model for the HH and VV channel intensity data with mean and number of looks as the two free parameters. Test statistics are often used to design hypothesis tests after theoretically deriving their asymptotic distributions. Such hypothesis tests, to have practical utility, require a judicious choice of sample size, and a level of significance for thresholding respectively. Instead of relying on the validity of the asymptotic distribution of test statistics (i.e. an implicit assumption for using p-values and levels of significance), in this work we use the test statistics as direct quantifiers of change. We apply a simple k-means clustering with k = 2 to these quantifiers in order to segregate change and no-change regions. With these, we show that the both information theoretic measures provide substantive evidence for change detection. The corresponding change maps are studied together to understand the complementary nature of the selected statistics. It is inferred that these two statistics may be used in tandem for better change detection analysis in SAR imagery.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| 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".