Crop classification based on G-CNN using multi-scale remote sensing images
Bibliographic record
Abstract
Crop classification is important for monitoring crop growth and ensuring national food security. Crop plots have a complex spatial planting structure with a certain degree of fragmentation, which leads to mixing of different crops with unclear borders, in turn affecting the ability of the classification model to recognize and distinguish between crops. To address the above problems, this paper proposes a crop classification method based on multi-source optical remote sensing data, which can extract crop information more accurately by taking advantage of the multi-spectral features and different spatial resolutions of different optical data. Firstly, spectral bands and vegetation indexes are extracted from Sentinel-2B and Jilin Gaofen (JLGF02B). Then, a Gemel Convolutional Neural Network (G-CNN) is constructed to fully mine and integrate the feature information at different scales for crop classification. Finally, the method is compared with classic deep learning algorithms. The result shows that the proposed G-CNN algorithm gets the best results with an overall accuracy OA of 96.5% and a Kappa coefficient of 94.7%. The G-CNN model provides a new idea and method for crop classification using multi-scale optical data.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".