LOESS radiometric correction for contiguous scenes (LORACCS): Improving the consistency of radiometry in high-resolution satellite image mosaics
Bibliographic record
Abstract
Analyses of Planet Dove (CubeSat) satellite imagery can prove challenging when radiometric consistency among contiguous scenes is necessary to apply algorithms across mosaics or through time. While temporal resolution is excellent due to the high number of Dove satellites, the radiometric consistency between scenes can be a limiting factor in the utility of the imagery when creating mosaics. To date, methods of addressing the radiometric consistency problem have focused on radiometric correction in a time-series stack, but do not address how to achieve seamless mosaics within that stack. We demonstrate a method to correct Planet Dove radiometric inconsistencies in mosaics using locally estimated scatterplot smoothing (LOESS). LOESS is a non-parametric regression technique that is more flexible to fitting a line to data than ordinary least squares regression (OLS), and better conforms to the nonlinearities in spectral responses among Dove sensors across brightness levels. A straightforward application of LOESS uses overlap areas between contiguous scenes, enabling correction independent of outside data sources. We demonstrate the effectiveness of the method to improve the quality of mosaics by reducing spectral discontinuities between overlapping images in multiple locations across multiple biome and landcover types. Further, the LOESS Radiometric Correction for Contiguous Scenes, or LORACCS, is able to be integrated with methods previously developed to normalize Dove imagery with sensors such as Landsat or Sentinel-2, typically for use in time series analysis. Written in Python, this method offers an open-source solution to a significant challenge when working with Dove data.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".