A New Robust Approach for Remote Sensing Image Regional Classification
Why this work is in the frame
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Bibliographic record
Abstract
The main problem of remote sensing image classification is the contradiction of classification precision and algorithm complexity,and algorithm lacking of robust.Therefore,a multi-model robust approach of remote sensing image classification based on non-parameter kernel density estimation of resampling strategy in feature space and edge detection is proposed in this paper.The edge gradient and direction information are obtained by edge detection of remote sensing.Then the new samples sets are weighted mean shift filtering to find kernel density function local maximum of image each region using resampling strategy in the joint spatial-range domain and data points are shifted the local maximum by iterative shifting.Last,the classification image is obtained by combining each region.Experimental results illustrate that it is able to classify remote sensing image effectively and robustly.
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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