Enhanced Segmentation Accuracy in High-Resolution Remote Sensing Images Using a Multi-Scale Convolutional Network
Bibliographic record
Abstract
Due to the traditional high-resolution Remote Sensing Image Segmentation (RSIS), the efficiency of the model algorithm is very low and the accuracy is also very poor. We design a new machine learning segmentation network architecture composed of a full convolutional network framework. The whole structure is tightly connected, each layer can feed back to each other and the multi-scale convolution kernel is used to build a wider network to improve the adaptability of the network at different scales. Compared with other traditional models, it has higher segmentation accuracy. This paper also optimizes and improves the algorithm used in the model, which makes the algorithm in this paper have more excellent accuracy and recall and is superior to the traditional algorithm in all aspects and has more outstanding performance. The experimental results show that compared with the traditional models and algorithms, the accuracy of the proposed model for high-resolution RSIS is up to about 95% and it has good stability and less interference from the outside world. It is superior to the traditional machine learning segmentation network model in many aspects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".