Comparison of machine learning algorithms Random Forest, Artificial Neural Network and Support Vector Machine to Maximum Likelihood for supervised crop type classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The classification and recognition of agricultural crop types is an important application of remote sensing. New machine learning algorithms have emerged in the last years, but so far, few studies only have compared their performance and usability. Therefore, we compared three different state-of-the-art machine learning classifiers, namely Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) as well as the traditional classification method Maximum Likelihood (ML) among each other. For this purpose we classified a dataset of more than 500 crop fields located in the Canadian Prairies with a stratified randomized sampling approach. Up to four multi-spectral RapidEye images from the 2009 growing season were used. We compared the mean overall classification accuracies as well as standard deviations. Furthermore, the classification accuracy of single crops was analysed. Support Vector Machine classifiers using radial basis function or polynomial kernels exhibited superior results to ANN and RF in terms of overall accuracy and robustness, while ML exhibited inferior accuracies and higher variability. Grassland exhibited the best results for early-season mono-temporal analysis. With a multi-temporal approach, the highest accuracies were achieved for Rapeseed and Field Peas. Other crops, such as Wheat, Flax and Lentils were also successfully classified. The users and producers accuracies were higher than 85 %.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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