Projection Pursuit Feature Analysis for Pan-Sharpened Multispectral Ikonos Imagery
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
Orbiting multispectral (MS) sensors can facilitate feature discrimination in clutter since natural clutter and man-made objects often differ in the energy they radiate across the electromagnetic spectrum. The projection pursuit technique (PP) has been previously proposed for assessing information content in large multivariate data sets such as hyperspectral (HS) imagery. Although the number of spectral bands is limited in MS data sets, this study investigates the suitability of PP for target detection. PP can highlight different features of interest in an image, improving and simplifying subsequent detection. This study uses two data sets: (1) 4 m MS IKONOS data and (2) pan-sharpened 1 m IKONOS MS imagery created by fusing the 4m MS and the associated 1 m panchromatic image sets. It is shown that PP based on the information divergence index can facilitate detection of certain targets, and emphasize features in MS data. This paves the way for an automated target detection and recognition system based around a PP preprocessing procedure
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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