FMCNet: A Fuzzy Multiscale Convolution Network for Remote Sensing Image Segmentation
Bibliographic record
Abstract
Due to being affected by factors such as imaging distance, lighting, ground features, and environment, objects in the same class may have certain differences, and different classes of objects often produce similar visual features in remote sensing images. This phenomenon leads to an uncertainty problem in segmentation of remote sensing images, i.e., intra-class heterogeneity and inter-class blurring. To alleviate this problem, a fuzzy multiscale convolution neural network (FMCNet) is proposed in this paper. By extracting receptive fields of different scales, sizes and aspect ratios, the detailed information in remote sensing objects is fully represented. The relationship between their adjacent pixels is effectively expressed by fuzzy logic learning to alleviate the uncertain segmentation. The proposed method achieves overall accuracies of 85.33%, 86.31%, and 85.39% on the Vaihingen, Potsdam, and Gaofen Image datasets respectively. It demonstrates superior performance compared to existing popular methods.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".