Data fusion of multiple polarimetric SAR images using discrete wavelet transform (DWT)
Bibliographic record
Abstract
Data fusion is a very effective technique which can be applied to many remote sensing areas such as classification, monitoring of environmental surveillance and man-made target tracking. In this paper, we tested fusion of multiple frequency (C-, and L-band), multiple polarization (HH, HV and VV) and multiresolution data sets. One can obtain a polarimetric SAR data after enhancing spatial resolution through the image fusion process. In order to fuse multiple SAR data and high spatial resolution data, they have to be geometrically co-registered over the same target area and have the same pixel size (spatial registrations). At this stage, we used the nearest neighbor resampling to avoid spectral distortion by interpolation. Multiresolution polarimetric SAR image fusion was performed using the multiscale image fusion technique-discrete wavelet transform after spatial registrations. To evaluate the spectral fidelity of fused polarimetric SAR data, spectral dissimilarity was calculated at each wavelet decomposition level. The resulting classification map based on polarimetric feature vectors shows better class separation after application of fusion processing than without fusion. The polarimetric SAR data over the Gong-ju areas, tested in this research, were acquired during NASA/JPL AIRSAR PACRIM-II experiment in 2000.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".