Landsat-8 and Sentinel-2 Image Fusion Based on Multiscale Smoothing-Sharpening Filter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the increasing demand for high temporal and spatial resolution multispectral image sequences, many studies have been carried out on fusion on Landsat-8 and Sentinel-2 images to obtain image sequences with a revisit cycle of 2 and 3 days and a spatial resolution of 10 m. However, current fusion methods suffer from complex computation and loss of spectral and spatial information. To address these issues, a Landsat-8 and Sentinel-2 image fusion based on multiscale smoothing-sharpening filter (MSSF) method is proposed. MSSF combines well the initial spatial prediction obtained from the Landsat-8 image at the target date and the detailed image extracted from the Sentinel-2 image at the reference date. Thin plate spline interpolation with morphological opening-closing algorithm is implemented on the Landsat-8 image at the target date, and the Laplacian of Gaussian enhancement algorithm is applied to the Sentinel-2 image at the reference date in the preprocessing stage. Smoothing-sharpening filter (SSIF) is employed to separate the high and low frequency components of the two preprocessed images. The multiscale SSIF is then utilized to migrate the details from the preprocessed Sentinel-2 image to the preprocessed Landsat-8 image. The performance of MSSF and five compared methods was evaluated qualitatively and quantitatively. Experiments on three remote sensing data sets gathered from different experimental sites confirm that the proposed MSSF method could efficiently generate Sentinel-2-like images with high spatial and spectral resolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it