A wavelet integrated image fusion approach for target detection in very high resolution satellite imagery
Bibliographic record
Abstract
Commercially available very high resolution satellite imagery has reached a sub-meter ground resolution for panchromatic imagery and a few meters of resolution for multispectral imagery (e.g., QuickBird panchromatic 0.6m and multispectral 2.4m). Ground targets such as vehicles can be clearly recognized in the panchromatic imagery, but difficult in the multispectral imagery. For automatic target detection, however, it is desired to have sub-meter multispectral imagery. This paper introduces a new wavelet integrated image fusion approach to produce a sub-meter multispectral image by combining a sub-meter panchromatic image with a several-meter multispectral image. The characteristics of the wavelet transform for spatial detail extraction and advantages of the IHS (Intensity Hue Saturation) fusion techniques are integrated. QuickBird panchromatic and multispectral images are fused. The results are compared with those of other existing image fusion techniques. Visual analyses demonstrate that the new wavelet integrated approach achieves better results for target detection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".