COMBINATION OF OBJECT-BASED AND PIXEL-BASED IMAGE ANALYSIS FOR CLASSIFICATION OF VHR IMAGERY OVER URBAN AREAS
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
Although spatial measures such as texture and shape extracted from very high resolution imagery (VHR) have been successfully employed in pixel-based classifications, the effectiveness of such measures in classification mainly depends on the optimal window size in which spatial measures are calculated. However, an optimal window size is usually subjective and varies for different image and different land cover types. Multiresolution segmentation of object-based image analysis, on the other hand, results objects with different size and shape, which are meaningful and better represent the real size and shape of land cover types. This paper introduces a new approach to land cover classification which benefits from both pixel-based and object-based image analyses. The VHR image is firstly segmented into different levels of segmentations. For each level, one set of spectral measures and two sets of spatial measures, texture and morphology, are extracted and then stacked to the original bands of VHR image forming a several-band image. To determine the contribution of each set of measures in separating urban land cover classes, the separability distance for all class pairs are calculated based on Bhattacharryya distance for each set of measures (i.e. spectral, texture and morphology). A pixel-based maximum likelihood classification is then applied to each set of bands. Results show that adding either texture or morphology to the original bands of VHR image has almost the same effect in increasing the overall classification accuracy. Furthermore, the classification accuracy of buildings and roads increases significantly by incorporation of spatial measures in classification 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