Satellite image fusion by using a combination of IHS and HPM methods
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
There are numerous image fusion techniques such as intensity-hue-saturation (IHS) transform and principal component analysis (PCA). These methods are offering promising performance but the drawback with them is that they are not necessarily optimal in newer applications such as Ikonos and QuickBird. Color distortion is of vital importance in fusion image processing. The main result of this paper is the development of a fast HPM-enhanced version of the IHS method for application in fusion image processing in high-resolution satellite images. Combining these two methods makes it possible to benefit from the advantages of both methods. To evaluate the HPM-enhanced version of IHS method we used QuickBird data. The HPM-enhanced version of IHS and HPM-enhanced IHS are used interchangeably. The simulation results of this method show that it is capable of providing a significant improvement in preserving spectral and spatial information.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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 it