THE EFFECT OF FOUR NEW MULTISPECTRAL BANDS OF WORLDVIEW2 ON IMPROVING URBAN LAND COVER CLASSIFICATION
Bibliographic record
Abstract
Conventional VHR imagery provides four multispectral (MS) bands. Built-up and traffic areas, however, are spectrally too similar to be distinguished using exclusively the spectral information of VHR imagery. The recently available WorldView2 (WV2) imagery introduces four new MS bands in addition to the four standard MS bands. This rich amount of spectral information together with the very high spatial resolution of WV2 imagery provides the potential for more robust and accurate discrimination between impervious land cover types. This paper aims to explore the contribution of the four newly added MS bands of WV2 imagery to increasing the class-pair separability of urban impervious land covers and consequently classification accuracy. For this, several object-based spectral and textural features of two data sets are extracted. The first data set consist of four standard MS bands, while the second one includes all eight MS bands of WV2. Then, a class-pair separability analysis is conducted to assess the contribution of new bands in discriminating different classes. Finally, the image is classified using each set of data separately. The effect of four new bands on land cover classification is evaluated by accuracy assessment of the results. Results demonstrate that the new four bands of WV increase the overall accuracy by 21.5 %. However, it is found that these new four bands will not have a significant effect on classification accuracy if additional textural and, specially, spectral feature of segmented image are utilized in the classification process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".