Multiscale Cascaded Network for the Semantic Segmentation of High-Resolution Remote Sensing Images
Bibliographic record
Abstract
As remote sensing images have complex backgrounds and varying object sizes, their semantic segmentation is challenging. This study proposes a multiscale cascaded network (MSCNet) for semantic segmentation. The resolutions employed with respect to the input remote sensing images are 1, 1/2, and 1/4, which represent high, medium, and low resolutions. First, 3 backbone networks extract features with different resolutions. Then, using a multiscale attention network, the fused features are input into the dense atrous spatial pyramid pooling network to obtain multiscale information. The proposed MSCNet introduces multiscale feature extraction and attention mechanism modules suitable for remote sensing land-cover classification. Experiments are performed using the Deepglobe, Vaihingen, and Potsdam datasets; the results are compared with those of the existing classical semantic segmentation networks. The findings indicate that the mean intersection over union (mIoU) of the MSCNet is 4.73% higher than that of DeepLabv3+ with the Deepglobe datasets. For the Vaihingen datasets, the mIoU of the MSCNet is 15.3%, and 6.4% higher than those of a segmented network (SegNet), and DeepLabv3+, respectively. For the Potsdam datasets, the mIoU of the MSCNet is higher than those of a fully convolutional network, Res-U-Net, SegNet, and DeepLabv3+ by 11.18%, 5.89%, 4.78%, and 3.03%, respectively.
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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 | 0.002 |
| 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".