Radiometric Normalization of Multi-temporal High Resolution Satellite Images with Quality Control for Land Cover Change Detection
Bibliographic record
Abstract
The radiometric normalization of multitemporal satellite optical images of the same terrain is often necessary for land cover change detection, e.g., relative differences. In previous studies, ground reference data or pseudo-invariant features (PIFs) were used in the radiometric rectification of multitemporal images. Ground reference data are costly and difficult to acquire for most satellite remotely sensed images and the selection of PIFs is generally subjective. In addition, previous research has been focused on radiometric normalization of two images acquired on different dates. The problem of conservation of radiometric resolution in the case of radiometric normalization between more than two images has not been addressed. This article reports on a new procedure for radiometric normalization between multitemporal images of the same area. The selection of PIFs is done statistically. With quality control, principal component analysis (PCA) is used to find linear relationships between multitemporal images of the same area. The satellite images are normalized radiometrically to a common scale tied to the reference radiometric levels. The procedure ensures the conservation of radiometric resolution for the multitemporal images involved. The new procedure is applied to three Landsat-5 Thematic Mapper (TM) images from three different years (August 1986, 1987, and 1991) and of the same area. Quality control measures show that the error in radiometric consistency between the multitemporal images is reduced effectively. The Normalized Difference Vegetation Index (NDVI) is calculated using the radiometrically normalized multitemporal imagery and assessed in the context of land cover change analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".