Urban Classification Of High Resolution Ikonos Imagery Using Texture
Why this work is in the frame
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Bibliographic record
Abstract
Traditional spectral-based methods of extracting urban land cover and land use information from remote sensing imagery have proven to be unsuitable for high spatial resolution images. Texture has been widely investigated as a supplement to spectral data for the analysis of complex urban scenes. This research evaluates the Grey Level Co-occurrence Matrix (GLCM) texture analysis technique and the Maximum Likelihood Classification approach for the extraction of texture features to be combined with spectral data, as a method for obtaining more accurate urban land cover and land use information from high spatial resolution images. Classifications were performed on IKONOS imagery using three datasets: a spatial dataset consisting of three texture images (mean, homogeneity and dissimilarity), a spectral dataset consisting of four spectral images (red, green, blue and N-IR), and a combination dataset (spatial and spectral). Results show that the combination dataset produced the highest overall classification accuracy of 86.1%, an improvement of 7.2% over the spectral dataset.
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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 | 0.000 |
| 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