Spectral Information Analysis from Multisensor Image Fusion for Land Use/Land Cover Classification in a Tropical Area: A Case Study in Bogor, Indonesia
Bibliographic record
Abstract
This paper evaluates the analysis of spectral information using RADARSAT, SPOT Panchromatic, and Landsat-TM images for land use/land cover classification in a tropical area. The case study is of Bogor, located in West Java, Indonesia. In order to increase spectral contrast of the Landsat-TM image, two procedures for information compression, colour composite and PCA (Principal Component Analysis) were tested. The best five RGB colour composites were produced, which were visually and statistically investigated, including the RGB colour composites of PC-1, PC-2, and PC-3, which were created from standard and selective principal component analysis (PCA), respectively. To merge images, two spectral-based image fusion techniques were applied: IHS (Intensity, Hue, and Saturation) and Brovey transforms. A land use/land cover classification using IHS transform from the RGB colour composites of PC-1, PC-2, and PC-3, where the intensity was replaced by SPOT Panchromatic, was the best result. It has an overall accura...
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".